Systems and methods for improved health care cohort reporting

ABSTRACT

A method for generating a report by a computing device is described. The method includes identifying a specific health care intervention. The method also includes creating a health care cohort for the specific health care intervention. The health care cohort includes a definition of a primary intervention. The method further includes generating a report based on the health care cohort.

RELATED APPLICATIONS

This application is related to and claims priority from U.S. ProvisionalPatent Application Ser. No. 62/129,480, filed Mar. 6, 2015 for “SYSTEMSAND METHODS FOR IMPROVED COMPARISON OF HEALTH CARE COSTS, UTILIZATIONAND OUTCOMES,” which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to health-related systems andtechnologies. More specifically, the present disclosure relates tosystems and methods for improved health care cohort reporting.

BACKGROUND

Computer and communication technologies continue to advance at a rapidpace. Indeed, computer and communication technologies are involved inmany aspects of a user's day. Computers commonly used include everythingfrom hand-held computing devices to large multi-processor computersystems. Computer technology is becoming increasingly important in themedical services environment. For example, computers may assist healthcare providers in treating patients. Also, computer systems may be usedin the medical environment to assist clinicians and other health careproviders.

Health care providers often find that utilization rates of health caresupplies and equipment, costs, and medical outcomes as well as patientoutcomes for certain health care interventions vary significantly andare difficult to predict. Current analytic products on the market maynot provide the detailed accuracy necessary to generate true comparisonsin the cost, utilization rates, and medical outcomes of health careinterventions. Without accurate comparisons, it is difficult for healthcare providers to set or reach goals for financial savings or improvedpatient outcomes and to predict medical costs for patients. Therefore,improvements in systems and methods for generating detailed analyticreports in the medical services environment may be beneficial.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating one example of a computing devicein which systems and methods for generating a report may be implemented;

FIG. 2 is a flow diagram illustrating one configuration of a method forgenerating a report;

FIG. 3 is a block diagram illustrating an example of a health carecohort identifier;

FIG. 4 is a flow diagram illustrating a more specific configuration of amethod for generating a report;

FIG. 5 is a flow diagram illustrating one configuration of a method forextracting health care cohort data;

FIG. 6 is a flow diagram illustrating one configuration of a method fororganizing a health care cohort;

FIG. 7 is a flow diagram illustrating one configuration of a method forassigning a primary health care provider;

FIG. 8 is a flow diagram illustrating a configuration of a method forcleaning data;

FIG. 9 is a flow diagram illustrating a method for analyzing data trendsin database tables;

FIG. 10 is a flow diagram that illustrates a more specific configurationof a method for analyzing data to determine data quality issues;

FIG. 11 is a flow diagram illustrating a method for populating one ormore database tables;

FIG. 12 is a flow diagram illustrating one configuration of a method forgenerating one or more reports based on data from database tables;

FIG. 13 is a flow diagram illustrating a method for defining a healthcare cohort status;

FIG. 14 is a flow diagram illustrating a method 1400 for refining apreliminary cohort; and

FIG. 15 illustrates various components of a computing device that may beimplemented in accordance with one or more of the systems and methodsdisclosed herein.

DETAILED DESCRIPTION

A method for generating a report by a computing device is described. Themethod includes identifying a specific health care intervention. Themethod also includes creating a health care cohort for the specifichealth care intervention. The health care cohort includes a definitionof a primary intervention. The method further includes generating areport based on the health care cohort. The method may include cleaninghealth care cohort data.

The health care cohort may include a list of associated acceptablesecondary interventions. The health care cohort may include a list ofexclusionary secondary interventions that if performed would exclude acorresponding encounter from the health care cohort.

The report may include a system-specific report. The system-specificreport may include a hospital-specific or site-specific report. Thereport may include a health care provider-specific report. The reportmay include a list of cohorts organized relative to a cost variation perencounter compared to an average.

An apparatus for generating a report is also described. The apparatusincludes a processor. The apparatus also includes memory in electroniccommunication with the processor. The apparatus further includesinstructions stored in the memory. The instructions are executable toidentify a specific health care intervention. The instructions are alsoexecutable to create a health care cohort for the specific health careintervention. The health care cohort includes a definition of a primaryintervention. The instructions are further executable to generate areport based on the health care cohort.

A method for refining a health care prehort by a computing device isalso described. The method includes performing a preliminary extractionfrom one or more databases based at least on a primary interventioncode. The method also includes storing a set of encounters in a healthcare prehort table. The method further includes generating a userinterface control that provides a selection between at least twoanatomical options or at least two health care intervention options. Themethod additionally includes, for each encounter in the health careprehort table, presenting an encounter from the health care prehorttable on a user interface. The user interface includes the userinterface control for the encounter. The method also includes, for eachencounter in the health care prehort table, receiving a selectedanatomical option or a health care intervention option. The methodfurther includes, for each encounter in the health care prehort table,adding the encounter to a health care cohort if the selected anatomicaloption or health care intervention option qualifies the encounter forinclusion in the health care cohort. The method additionally includesgenerating one or more reports based on the health care cohort. Themethod may include highlighting one or more keywords in clinical notesfor the encounter.

An apparatus for refining a health care prehort is also described. Theapparatus includes a processor. The apparatus also includes memory inelectronic communication with the processor. The apparatus furtherincludes instructions stored in the memory. The instructions areexecutable to perform a preliminary extraction from one or moredatabases based at least on a primary intervention code. Theinstructions are also executable to store a set of encounters in ahealth care prehort table. The instructions are further executable togenerate a user interface control that provides a selection between atleast two anatomical options or at least two health care interventionoptions. The instructions are additionally executable to, for eachencounter in the health care prehort table, present an encounter fromthe health care prehort table on a user interface. The user interfaceincludes the user interface control for the encounter. The instructionsare also executable to, for each encounter in the health care prehorttable, receive a selected anatomical option or a health careintervention option. The instructions are further executable to, foreach encounter in the health care prehort table, add the encounter to ahealth care cohort if the selected anatomical option or health careintervention option qualifies the encounter for inclusion in the healthcare cohort. The instructions are additionally executable to generateone or more reports based on the health care cohort.

Health care providers often find that costs, utilization rates, andoutcomes for certain health care interventions vary significantly andare difficult to predict. Some products provide detailed utilizationanalytics for health care providers, but those products' health careintervention comparisons include but are not limited to medical billingprocedure codes or diagnostic codes, such as InternationalClassification of Diseases (ICD) (e.g., ICD-9, ICD-10, other ICD codes,etc.) or Current Procedural Terminology (CPT) codes, as well as otherwidely recognized coding terminologies identified in the national healthcare registries. These comparisons are generally termed cost-per-casereports. By generating cost-per-case reports and outcome comparisonsusing general billing codes organized under one or more codes, thefindings may not account for variability in individual health careinterventions, such as, for example, whether the intervention was aprimary or secondary intervention and differences in potentiallyrelevant demographic information. Accordingly, current analytic productson the market may not provide the detailed accuracy necessary togenerate true comparisons in the cost, utilization rates, and outcomesof health care interventions. Without accurate comparisons of healthcare interventions, it is difficult for health care providers to set orreach goals for financial savings or improved patient outcomes and topredict medical costs for patients. Therefore, improvements in systemsand methods for generating detailed analytic reports in the medicalservices environment may be beneficial.

Systems and methods for improved comparison of health care interventioncosts, utilization rates, and outcomes in the health care environmentare described. The systems and methods include identifying health careinterventions, which represents a key improvement over existing systemsand methods for health care intervention cost-per-case reports. A healthcare intervention may be (e.g., may represent) an action taken by ahealth care provider in behalf of a patient and/or a record of one ormore actions taken by a health care provider in behalf of a patient.Examples of health care interventions may include primary interventions,acceptable secondary interventions and/or secondary interventions thatif performed would exclude a particular encounter from being included inthe health care cohort. For example, a health care cohort may be aclinically defined group of patient encounters that may be identifiedusing one or more parameters (e.g., existing data codes, newly createddata codes, data from one or more hospital information systems (HIS),etc.). A health care cohort may be defined in terms of one or moreinterventions. For instance, a health care cohort may include encountersthat may involve a primary intervention and/or a defined list ofassociated acceptable secondary interventions, but that may not involvea defined list of secondary interventions that if performed wouldexclude a particular encounter from being included in the health carecohort. An encounter may represent (e.g., may be a record of) aninteraction between a patient and a health care provider. A health careprovider may request the identification of one or more health carecohorts if he or she is not satisfied with existing cost-per-case datathat is currently limited to analysis of specific billing procedure ordiagnostic codes, including but not limited to ICD or CPT codes, orother coding terminology from the national health care registries. Inthis improved approach, the queries for health care cohorts may becreated using existing and widely recognized health care terminologies,including but not limited to ICD procedure codes and CPT codes, as wellas multiple other criteria to ensure true comparison of similar healthcare interventions. The method for identifying a health care cohort willbe further described below.

The systems and methods also include generating detailed analyticreports based on the selected health care cohort. The reports may beused to review and prepare data to share with health care providers andhealth care administrators. The report may display a site-specific(e.g., hospital-specific, clinic-specific, surgical suite-specific,etc.) list of health care cohorts organized relative to the variation ofcost compared to the system average. The report may also show how aspecific health care cohort's system average compares by region average,by one or more individual facilities, and/or by physician. The reportallows customization of health care cohorts related to date range, age,severity of illness, gender, and insurance type. The type of cost thatis displayed may also be customized.

Comparative data in the report may include variables specific to medicalservices, such as operating room time, staffing, and supplies used. Thereport may also include utilization data for length of stay,nursing/floor costs, lab costs, pharmacy costs, post-anesthesia careunit (PACU) time, average PACU charge, therapy costs, imaging costs, andother costs. Users may view a trend graph that depicts the selectedhealth care providers' trend for the previous year for time, volume ofcases, supply costs, supply charges, total costs, and the reimbursementa hospital or network received for the selected health care cohort.

The systems and methods disclosed herein may allow for improvedcomparison of health care intervention costs and outcomes. For example,these systems and methods may allow a medical provider to moreaccurately understand how costs and outcomes for a specific health careintervention compares to the system average, region average, and how itcompares for individual facilities. Further, this method for identifyinga health care cohort and generating detailed comparison reports may bebeneficial for health care providers seeking to set or reach budget andoutcome goals and to more accurately predict costs and outcomes forpatients.

The systems and methods disclosed herein may fulfill a need in themedical industry. For example, existing approaches to quantify medicalcosts may not provide specific health care intervention implementationcomparisons. For example, existing approaches may not offer specificdata that illustrate specific routes to costs savings. However, thesystems and methods disclosed herein may offer comparisons of encounterswhere homogenous and specific health care interventions were performed.This provides specific routes to cost savings, in that cost-savingexecution of specific health care interventions may be identified. Forexample, differences in supply usage between performances of a specifichealth care intervention may illustrate less costly supplies and/orquantities needed to perform the specific health care intervention. Thesystems and methods disclosed herein may facilitate cost savings in themedical industry.

Various configurations are now described with reference to the figures,where like reference numbers may indicate functionally similar elements.The systems and methods as generally described and illustrated in thefigures herein could be arranged and designed in a wide variety ofdifferent configurations. Thus, the following more detailed descriptionof several configurations, as represented in the figures, is notintended to limit scope, as claimed, but is merely representative of thesystems and methods.

FIG. 1 is a block diagram illustrating one example of a computing device102 in which systems and methods for generating a report may beimplemented. In some configurations, the report may compare health careintervention costs, utilization rates, and/or outcomes. Examples of thecomputing device 102 include computers (e.g., desktop computers, laptopcomputers, servers, supercomputers, etc.), smart phones, tablet devices,health care equipment, etc. The computing device 102 may include one ormore components or elements. One or more of the components or elementsmay be implemented in hardware (e.g., circuitry) or a combination ofhardware and software (e.g., a processor with instructions).

In some configurations, the computing device 102 may perform one or moreof the functions, procedures, methods, steps, etc., described inconnection with one or more of FIGS. 1-14. Additionally oralternatively, the computing device 102 may include one or more of thestructures described in connection with one or more of FIGS. 1-14.

In some configurations, the computing device 102 may include one or moreprocessors 104, memory 106 (e.g., one or more memory circuits, randomaccess memory (RAM) circuits, RAM chips, etc.), one or more displays 112and/or one or more communication interfaces 108. The processor 104 maybe coupled to (e.g., in electronic communication with) the memory 106,display 112 and/or communication interface 108. It should be noted thatone or more of the elements of the computing device 102 described inconnection with FIG. 1 (e.g., communication interface(s) 108, display(s)112, etc.) may be optional and/or may not be included (e.g.,implemented) in the computing device 102 in some configurations.

The processor 104 may be a general-purpose single- or multi-chipmicroprocessor, a special-purpose microprocessor (e.g., a digital signalprocessor (DSP)), a microcontroller, a programmable gate array, etc. Theprocessor 104 may be referred to as a central processing unit (CPU).Although just a single processor 104 is shown in the computing device102, in an alternative configuration, a combination of processors couldbe used. The processor 104 may be configured to implement one or more ofthe methods disclosed herein. The processor 104 may include and/orimplement a health care cohort identifier 116, a cohort creator 118,and/or a report generator 120 in some configurations.

The memory 106 may be any electronic component capable of storingelectronic information. For example, the memory 106 may be implementedas random access memory (RAM), read-only memory (ROM), magnetic diskstorage media, optical storage media, flash memory devices in RAM,on-board memory included with the processor, EPROM memory, EEPROMmemory, registers, and so forth, including combinations thereof.

The memory 106 may store instructions and/or data. The processor 104 mayaccess (e.g., read from and/or write to) the memory 106. Theinstructions may be executable by the processor 104 to implement one ormore of the methods described herein. Executing the instructions mayinvolve the use of the data that is stored in the memory 106. When theprocessor 104 executes the instructions, various portions of theinstructions may be loaded onto the processor 104 and/or various piecesof data may be loaded onto the processor 104. Examples of instructionsand/or data that may be stored by the memory 106 may include image data,health care cohort identifier 116 instructions, cohort creator 118instructions and/or report generator 120 instructions, etc.

The communication interface(s) 108 may enable the computing device 102to communicate with one or more other electronic devices 110. Forexample, the communication interface(s) 108 may provide one or moreinterfaces for wired and/or wireless communications. In someconfigurations, the communication interface(s) 108 may be coupled to oneor more antennas for transmitting and/or receiving radio frequency (RF)signals. Additionally or alternatively, the communication interface 108may enable one or more kinds of wireline (e.g., Universal Serial Bus(USB), Ethernet, etc.) communication.

In some configurations, multiple communication interfaces 108 may beimplemented and/or utilized. For example, one communication interface108 may be an Ethernet interface, another communication interface 108may be a universal serial bus (USB) interface, another communicationinterface 108 may be a wireless local area network (WLAN) interface(e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11interface) and yet another communication interface 108 may be a cellular(e.g., 3G, Long Term Evolution (LTE), CDMA, etc.) interface.

The computing device 102 and/or one or more electronic devices 110 maystore medical information. Medical information may include diagnosisinformation, treatment (e.g., treatment procedure) information, billinginformation, medical records and/or inventory information, etc. Forinstance, medical information may be stored in one or more databases(e.g., in an Enterprise Data Warehouse (EDW)). In some configurations,the medical information may be stored in a repository (e.g., EDW) thatintegrates multiple disparate sources (e.g., medical records database,billing database, imaging database, hospital information system (HIS),picture archiving and communication system (PACS), radiology informationsystem (RIS), etc.). In some configurations, the communication interface108 may receive information (e.g., medical information, health careinformation, medical records, billing records, etc.) from one or moreelectronic devices 110 (e.g., a remote server, another computing device,networked storage, etc.) and/or send information (e.g., medicalinformation, health care information, cohort information, reportinformation, etc.) to one or more electronic devices 110. Additionallyor alternatively, the computing device 102 may access medicalinformation that is stored on the computing device 102.

The display(s) 112 may be integrated into the computing device 102and/or may be coupled to the computing device 102. Examples of thedisplay(s) 112 include liquid crystal display (LCD) screens, lightemitting display (LED) screens, organic light emitting display (OLED)screens, plasma screens, cathode ray tube (CRT) screens, etc. In someimplementations, the computing device 102 may be a smartphone with anintegrated display. In another example, the computing device 102 may becoupled to one or more remote displays 112 and/or to one or more remotedevices that include one or more displays 112.

In some configurations, the computing device 102 may present a userinterface 114 on the display 112. For example, the user interface 114may enable a user to interact with the computing device 102. In someconfigurations, the user interface 114 may enable a user to inputinformation. For example, the user interface 114 may receive text input(from a keyboard, for instance), a mouse click, a touch, a gestureand/or some other input.

The processor 104 may include and/or implement a health care cohortidentifier 116. The health care cohort identifier 116 may identify oneor more specific health care interventions. A specific health carecohort may be defined in accordance with a health care intervention thatis carried out in a specific way. For example, a specific health carecohort may be defined by a health care intervention that follows one ormore specific steps (e.g., techniques) on a specific anatomy and/or thatis distinguished from other health care interventions in the anatomyand/or execution of one or more steps (e.g., techniques). A specifichealth care cohort may be more specific than a general health careintervention (e.g., a category of health care intervention). Forexample, a “spinal fusion” may be a general health care intervention,while a 1 level spinal fusion, 2 level spinal fusion, 3 level spinalfusion, etc., may be examples of specific health care interventions. Inanother example, a hysterectomy may be a general health careintervention, although there are multiple ways to carry out ahysterectomy. Examples of hysterectomies that may define specific healthcare cohorts may include abdominal hysterectomy, a laparoscopichysterectomy, a robotic laparoscopic hysterectomy (e.g., laparoscopichysterectomy using the Da Vinci surgical system, etc.), etc.

A specific health care cohort may be mutually exclusive from anotherspecific health care cohort (which may fall under the same generalhealth care intervention). For example, a specific health careintervention may include one or more elements (e.g., techniques, tools,steps, anatomy, etc.) that are different from (e.g., different from, notincluded in, etc.) another specific health care intervention. Forinstance, a laparoscopic hysterectomy may utilize a laparoscope, whilean abdominal hysterectomy may not. In another example, a 2 level spinalfusion may involve anatomy (e.g., another level of the spine) that isnot involved in a 1 level spinal fusion. A specific health care cohortmay be more specific than standard medical codes. For example, astandard medical code (e.g., CPT code) may only have one code for a“spinal fusion,” even though there may be several kinds of spinalfusions, which may be based on the anatomy involved (e.g., number ofspinal levels, location of spinal levels, etc.) and/or proceduresutilized to perform the spinal fusion. It should be noted that for somehealth care cohorts, there may be little procedural and/or anatomicalvariation between interventions. For example, some health care cohortsmay include health care interventions performed only one way and on thesame anatomy in all cases or encounters. Accordingly, a specific healthcare cohort may include a health care intervention that is onlyperformed one way and on the same anatomy in all cases or encounters.

In some configurations, the health care cohort identifier 116 mayidentify one or more specific health care interventions based onreceived input. For example, the health care cohort identifier 116 mayreceive an input (e.g., text input from a keyboard, a mouse click, etc.)that indicates a specific health care intervention. In some approaches,the received input may include the name of a specific health careintervention. For example, a health care provider (e.g., surgeon), datamanager and/or other user may input an identifier (e.g., name) of aspecific health care intervention.

In some configurations, the health care cohort identifier 116 maydetermine a primary intervention code that covers the specific healthcare intervention. For example, the health care cohort identifier 116may receive an input that indicates the primary intervention code (e.g.,ICD code, CPT code, billing code, etc.). A primary intervention code mayindicate a health care intervention group. A health care interventiongroup may be a group of specific primary intervention codes. Forexample, a primary intervention code that generally indicates “spinalfusion” may cover many types of spinal fusions (e.g., 2 level spinalfusion, 3 level spinal fusion, etc.). In some approaches, the healthcare cohort identifier 116 may automatically determine the primaryintervention code that covers the specific health care intervention. Forexample, the health care cohort identifier 116 may search for and/orselect one or more primary intervention codes that generally match aspecific health care health care intervention.

Additionally or alternatively, the health care cohort identifier 116 mayperform cost and/or encounter analysis. For example, the health carecohort identifier 116 may perform analysis on one or more health careintervention groups. A health care intervention group may be a group ofhealth care interventions that may be indicated based on one or moreparameters. For example, one or more primary intervention codes (e.g.,standard medical codes, ICD codes, CPT codes, etc.) may indicate ahealth care intervention group. In some approaches, the health carecohort identifier 116 may request and/or retrieve medical information(e.g., clinical detail, costs, billing information, etc.) pertaining tothe health care intervention group(s) (associated with one or morestandard medical codes, for example). The health care cohort identifier116 may analyze the medical information to determine one or moremeasures. For example, the health care cohort identifier 116 maydetermine one or more statistical measures (e.g., average, standarddeviation, variance, coefficient of variation, etc.) for encounters ineach health care intervention group. For instance, the health carecohort identifier 116 may determine a proportion of encounters withcosts that are more than one standard deviation from the average, maydetermine a total dollar amount of encounters with above average cost,may determine the coefficient of variation in the cost of theencounters, and/or may determine the contribution to margin. Forexample, the coefficient of variation and/or contribution to margin maybe metrics that may help identify whether a physician and/or hospital isperforming better or worse than their counterparts and may represent anopportunity for a deeper dive into the data. The coefficient ofvariation may represent the degree of dispersion from the mean. Forexample, assume hospital A did five appendectomy surgeries. Their totalcosts were 1100, 1000, 1050, 975 and 900 with a mean of 1005. It shouldbe noted that costs may be expressed in currency value (e.g., dollars)or in other units. Hospital B did five appendectomy surgeries and theirtotal costs were 805, 1005, 1205, 705, 1305 (mean of 1005). In thissituation both hospitals have the same mean cost but hospital B wouldhave a greater coefficient of variation. Contribution to margin may behow much money the hospital profits off a particular surgery. Ifhospital A has a greater contribution to margin for appendectomysurgeries than hospital B, it may be beneficial to determine whyhospital A has a greater contribution to margin.

Based on the analysis, the health care cohort identifier 116 mayidentify one or more health care interventions. For example, the healthcare cohort identifier 116 may indicate one or more encounters thatexhibit opportunities for cost savings and/or increased margin. Forinstance, the health care cohort identifier 116 may indicate one or moreencounters that have exceeded a threshold proportion of costs more thanone standard deviation from the average of the health care cohort.Additionally or alternatively, the health care cohort identifier 116 mayindicate one or more encounters based on a total dollar amount of casesor encounters with costs above a standard deviation from average cost(where the total dollar amount is above a threshold, for example).Additionally or alternatively, the health care cohort identifier 116 mayindicate one or more encounters that have a coefficient of variationabove a threshold. Additionally or alternatively, the health care cohortidentifier 116 may indicate one or more encounters that have acontribution to margin below a threshold. For one or more of theindicated encounters, the health care cohort identifier 116 may(automatically) generate a name for a specific health care intervention.Additionally or alternatively, the health care cohort identifier 116 maygenerate a message (for transmission or display, for example) requestinga name for a specific health care cohort identifier and/or suggestcreating one or more cohorts for each of the indicated health careintervention groups.

The processor 104 may include and/or implement a cohort creator 118. Thecohort creator 118 may create one or more health care cohorts for theone or more specific health care interventions. A health care cohort mayinclude a definition of a primary intervention (e.g., health careintervention, surgery, operation, treatment, etc.). A primaryintervention definition may include one or more parameters. For example,a primary intervention definition may include the specific health carecohort identifier (e.g., name), may include one or more primaryintervention codes and/or may include one or more other parameters(e.g., diagnosis code(s), billing group(s), associated providerspecialty(ies), patient demographics, etc.). More specific examples aregiven in connection with FIG. 6. A health care cohort may also include alist of one or more associated acceptable secondary interventions (ifany exist or are identified, for example). An acceptable secondaryintervention may be a health care intervention that may be performedwith the primary intervention. For example, an acceptable secondaryintervention may be performed in addition to the primary interventionwhile still being consistent with the health care cohort. For instance,if an acceptable secondary intervention accompanies a primaryintervention in an encounter, then the encounter may still qualify for(e.g., may be included within) the respective health care cohort. Oneexample of a primary intervention may be a spinal fusion with alaminectomy as an example of an acceptable secondary intervention.Additionally or alternatively, the health care cohort may include a listof one or more exclusionary secondary interventions (if any exist or areidentified, for example). An exclusionary secondary intervention may notbe performed in addition to the primary intervention while still beingconsistent with the health care cohort. For example, if an exclusionarysecondary intervention is performed in an encounter, then thatparticular encounter would be excluded from the health care cohort. Forexample, in a laparoscopic appendectomy cohort, the primary interventionmay be the removal of the appendix, but if a secondary intervention ofgallbladder removal is also done, this encounter may no longer meet thedefinition of the laparoscopic appendectomy cohort and will not beassigned to this cohort. In some approaches, this excluded encounter maybe assigned to an exclusionary health care cohort (e.g., health careexhort). In some configurations, creating a health care cohort mayinclude determining the primary intervention definition, determining anyacceptable secondary intervention(s), and/or determining anyexclusionary secondary intervention(s).

The cohort creator 118 may create one or more health care cohorts. Forexample, the cohort creator 118 may determine one or more encountersthat are consistent with the health care cohort (for inclusion in thehealth care cohort). For instance, the cohort creator 118 may determineone or more encounters where the primary intervention was performed.Optionally, the cohort creator 118 may determine one or more encounterswhere the primary intervention was performed, where (optionally) one ormore acceptable secondary interventions were performed, and/or where noexclusionary secondary intervention was performed. The one or moredetermined encounters may be included in the health care cohort (e.g.,listed in a health care cohort table).

In some configurations, creating a health care cohort may includeextracting data. For example, the cohort creator 118 may extract data(e.g., billing information, cost information, clinical notes (e.g.,operation notes, health care provider notes, health care providerdictation, etc.), diagnosis information (e.g., diagnosis description,diagnosis date(s), ICD code(s), etc.), treatment information (e.g.,treatment description, treatment date(s), CPT code(s), etc.), imaginginformation (e.g., x-ray(s), magnetic resonance imaging (MRI) data,photograph data, etc.), etc.). The data may be extracted from one ormore databases (e.g., a data warehouse, EDW, etc.) stored on thecomputing device 102 and/or on one or more of the electronic devices110. For example, the cohort creator 118 may extract the data bysearching the one or more databases for cases or encounters with one ormore criteria (e.g., ICD code(s), CPT code(s), keyword(s) in health careprovider notes, date range(s), patient information, etc.). For example,the cohort creator 118 may search the database(s) for cases orencounters that have a primary intervention code (e.g., CPT code)associated with a health care intervention group.

In some configurations, extracting the data may include performing apreliminary extraction (e.g., pull) from one or more databases (e.g.EDW). For example, the cohort creator 118 may utilize the primaryintervention code to extract cases or encounters with the primaryintervention code. In some configurations, extracting the data mayinclude determining one or more encounters for a preliminary cohort(e.g., health care “prehort”). In some configurations, the cohortcreator 118 may store the health care prehort encounters in a healthcare prehort table in one or more databases (e.g., in an EDW). A healthcare prehort may include one or more encounters (e.g., a health careintervention group) that may meet one or more cohort criteria (e.g.,primary intervention code), but where additional information may beutilized to make a determination whether the encounter(s) qualify forinclusion in the health care cohort.

For example, one or more cases or encounters with a primary interventioncode (e.g., medical code, ICD code, CPT code, etc.) indicating a spinalfusion may be extracted into a health care prehort (e.g., a health careprehort table). In some configurations, the primary intervention codemay not be specific enough to qualify a case or encounter for thecorresponding cohort. For example, a health care cohort may include onlyencounters where a 2 level spinal fusion was performed, while theprimary intervention code only indicates a spinal fusion in general.

In some approaches, the cohort creator 118 may arrange the health careprehort encounters for review. For example, the cohort creator 118 mayhighlight keywords (e.g., “level,” “L1,” “L2,” “L3,” “S1,” “S2,” etc.)in the clinical notes (e.g., operation notes) and present the clinicalnotes on the user interface 114. The cohort creator 118 may alsogenerate and present a selection control on the user interface 114 (withthe clinical notes, for example) for receiving input regarding whetherthe current encounter is a 2 level spinal fusion or not. The userinterface 114 may receive a selection, which the cohort creator 118 mayutilize to include the encounter in the health care cohort or excludethe encounter from the health care cohort. In some configurations, thecohort creator 118 may present a series of encounters on the userinterface 114, which may allow for efficient categorization of theencounters for the health care cohort. For example, this may obviate theneed for a health care provider to individually access the clinicalnotes for a set of encounters to determine whether an encounter shouldbe included or not.

The cohort creator 118 may determine one or more characteristics of thehealth care prehort (e.g., frequency of encounters with primaryintervention code, secondary interventions completed with the primaryintervention and/or frequency of secondary interventions, etc.). Forexample, the cohort creator 118 may identify a frequency of encounterswith a primary intervention code. For instance, the cohort creator 118may determine how often (e.g., average frequency of) encounters with theprimary intervention code arise. In some approaches, this may beaccomplished by determining a number of cases or encounters with theprimary intervention code over a period of time. The cohort creator 118may determine (e.g., identify) one or more secondary interventions thatmay be completed with the primary intervention. For example, the cohortcreator 118 may produce a list of secondary interventions that arecompleted with the primary intervention. In some approaches, the cohortcreator 118 may also determine a frequency of secondary interventions.For example, this may be accomplished by determining a number of casesor encounters with the secondary intervention (e.g., medical code) overa period of time.

In some approaches, the cohort creator 118 may determine one or moreexclusionary secondary interventions. For example, the cohort creator118 may determine a list of one or more exclusionary secondaryinterventions for a health care cohort. In some configurations, thecohort creator 118 may determine the one or more exclusionary secondaryinterventions automatically. For example, the cohort creator 118 maydetermine one or more encounters that are excluded from the health carecohort. In some instances, the encounter(s) may be excluded based on oneor more factors. For example, one or more encounters (which may have thesame primary intervention code, for instance) may be excluded from thehealth care cohort based on a received selection (e.g., the encounter isnot a 2 level spine fusion as described in the example above) and/orbased on one or more parameters (e.g., patient demographics, providerspecialty, hospital descriptor, clinical notes, etc.) in the primaryintervention definition. The cohort creator 118 may determine one ormore exclusionary secondary interventions in one or more encountersexcluded from the health care cohort, where the one or more exclusionarysecondary interventions are not found in any of the interventionsincluded in the health care cohort.

In some configurations, the cohort creator 118 may present the healthcare cohort (e.g., prehort) for review. For example, the cohort creator118 may present and/or send cohort information (e.g., primaryintervention information, acceptable secondary intervention information,exclusionary secondary intervention information and/or one or morehealth care cohort encounters, etc.). For instance, the cohort creator118 may present the health care cohort information on the display 112(e.g., user interface 114). The cohort creator 118 may receive one ormore change inputs. The change inputs may indicate one or more changesto the health care cohort information. For example, the change input(s)may specify adding one or more parameters (e.g., codes) to one or moreof the primary intervention information, acceptable secondaryintervention information and/or exclusionary secondary interventioninformation, etc. Additionally or alternatively, the change input(s) mayspecify removing one or more parameters to one or more of the primaryintervention information, acceptable secondary intervention informationand/or exclusionary secondary intervention information, etc.Additionally or alternatively, the change input(s) may specify addingone or more encounters. Additionally or alternatively, the changeinput(s) may specify removing one or more encounters. Presenting thehealth care cohort may allow a user (e.g., health care provider,physician, surgeon, nurse, etc.) an opportunity to change (e.g., addparameter(s) to, remove parameter(s) from, etc.) the health care cohort(e.g., prehort) such that the health care cohort accurately reflects thespecific health care intervention.

In a situation where no change inputs are received (and/or if a healthcare cohort is approved), the cohort creator 118 may organize the healthcare cohort. In some configurations, for instance, the cohort creator118 may organize the health care cohort. For example, the cohort creator118 may organize the parameters that define the health care cohort. Forinstance, the cohort creator 118 may organize the definition of theprimary intervention of the health care cohort. Organizing thedefinition of the primary intervention of the health care cohort mayinclude identifying (e.g., associating, storing, etc.) one or moreparameters for the primary intervention (e.g., the specific health careintervention). For example, the cohort creator 118 may identify (e.g.,associate, store, etc.) one or more intervention codes (e.g., ICD codes,CPT codes), one or more diagnosis codes (e.g., ICD codes), one or morebilling codes, one or more item types, one or more billing groups (e.g.,All Patient Refined Diagnosis Related Groups (APRDRGs), MedicareSeverity Diagnosis Related Groups (MSDRGs), etc.), one or moreregistries (e.g., Society of Thoracic Surgery (STS) database, etc.),illness severity, provider specialty, health care information systemintervention code, trauma level, number of encounters, patient status,patient demographic (e.g., age, gender, etc.), clinical notes and/orother parameter(s), etc., for the primary intervention of a health carecohort. Additionally or alternatively, organizing the health care cohortmay include identifying (e.g., associating, storing, etc.) one or moreparameters for the acceptable secondary intervention(s). For example,the cohort creator 118 may identify (e.g., associate, store, etc.) oneor more intervention codes (e.g., ICD codes, CPT codes), one or morediagnosis codes (e.g., ICD codes), one or more billing codes, one ormore item types, one or more billing groups (e.g., APRDRGs, MSDRGs,etc.), one or more registries (e.g., STS database, etc.), illnessseverity, provider specialty, health care information systemintervention code, trauma level, number of encounters, patient status,patient demographic (e.g., age, gender, etc.), clinical notes and/orother parameter(s), etc., for the acceptable secondary intervention(s)of a health care cohort. Additionally or alternatively, organizing thehealth care cohort may include identifying (e.g., associating, storing,etc.) one or more parameters for the exclusionary secondaryintervention(s). For example, the cohort creator 118 may identify (e.g.,associate, store, etc.) one or more intervention codes (e.g., ICD codes,CPT codes), one or more diagnosis codes (e.g., ICD codes), one or morebilling codes, one or more item types, one or more billing groups (e.g.,APRDRGs, MSDRGs, etc.), one or more registries (e.g., STS database,etc.), illness severity, provider specialty, health care informationsystem intervention code, trauma level, number of cases or encounters,patient status, patient demographic (e.g., age, gender, etc.), clinicalnotes and/or other parameter(s), etc., for the exclusionary secondaryintervention(s) of a health care cohort.

Upon organizing the health care cohort, the computing device 102 mayformalize the health care cohort. For example, the health care cohortmay be verified for further use. In a situation where one or more changeinputs are received, the cohort creator 118 may redefine and/orreorganize the health care cohort. For example, the cohort creator 118may update the health care cohort information. For instance, the cohortcreator 118 may add one or more parameters to the health care cohortinformation and/or may remove one or more parameters from the healthcare cohort information. In some approaches, the cohort creator 118 mayapply the change(s) indicated by the change input(s).

Additionally or alternatively, the cohort creator 118 may determine oneor more primary intervention parameters, one or more acceptablesecondary intervention parameters and/or one or more exclusionarysecondary intervention parameters. For example, if an input changeindicated an addition of an encounter, the cohort creator 118 maybroaden the health care cohort by adding one or more parameters to theprimary intervention and/or to the acceptable secondary intervention(s)(and/or by removing one or more parameters from the exclusionarysecondary intervention(s)) in order to include the encounter.Additionally or alternatively, if an input change indicated a removal ofan encounter, the cohort creator 118 may narrow the health care cohortby removing one or more parameters from the adding one or moreparameters to the exclusionary secondary intervention(s) (and/or byremoving one or more parameters from the primary intervention and/orfrom the acceptable secondary intervention(s)) in order to exclude theencounter. In some configurations, the cohort creator 118 may repeat(e.g., iterate) verification, redefinition and/or reorganizationoperations until the health care cohort is verified (e.g., until nochange inputs are received and/or until an approval for the health carecohort is received via the user interface 114, for example).

The cohort creator 118 may clean the health care cohort data. Forexample, the cohort creator 118 may identify one or more data qualityissues and resolve the one or more data quality issues. Data qualityissues may include conflicts and/or inconsistencies in one or morehealth care cohort encounters. For example, a provider specialty may notmatch a health care intervention in an encounter (e.g., a podiatristwould not have performed neurological surgery), dates may conflict(e.g., a discharge date occurs before an admittance date), billing costsmay conflict with the health care intervention, etc. In a situation thatone or more data quality issues are identified, the cohort creator 118may resolve the issue(s) and/or may remove one or more encounters withthe issue(s) from the health care cohort. More detail is given inconnection with one or more of FIGS. 4 and 7-9.

In some configurations, the cohort creator 118 may create one or morehealth care cohort tables. For example, the cohort creator 118 maypopulate one or more database tables based on the cleaned health carecohort (e.g., prehort) data. For instance, the cohort creator 118 mayadd one or more health care cohort encounters to one or more EDW tables.In some approaches, the cohort creator 118 may search one or moredatabases (e.g., an EDW) to find one or more encounters to include inthe health care cohort. This may be in addition to the preliminaryextraction performed previously. In some configurations, the health carecohort may be established when encounters are added to a health carecohort table. For example, a health care prehort may be converted to ahealth care cohort when the characteristics (e.g., primary interventiondefinition, list of acceptable secondary interventions, and/or list ofexclusionary secondary interventions, etc.) are established andencounters are added to the health care cohort table. In someconfigurations, one or more operations on data before the encounters areadded to the health care cohort table may be performed on a preliminaryhealth care cohort (e.g., prehort).

In some configurations, the cohort creator 118 may attempt to identifyany data quality issues. For example, the cohort creator 118 may iteratecleaning operations until no data quality issues remain. The cohortcreator 118 may provide the health care cohort to the report generator120.

The processor 104 may include and/or implement a report generator 120.The report generator 120 may generate a report based on the health carecohort. The report may include outcomes, costs and/or supplyutilization. For example, the report may include outcomes (e.g.,readmissions within a period) for one or more health care cohorts.Additionally or alternatively, the report may include costs for one ormore health care cohorts. For instance, the report may include anaverage cost for one or more health care cohorts. In one example, thereport may include a list of specific health care cohorts (e.g., healthcare cohorts) organized relative to a cost variation per health careintervention compared to an average. Additionally or alternatively, thereport may include supply utilization for one or more health carecohorts. For instance, the report may indicate supply types (e.g.,bandages, dressings, tapes, implants, gloves, intravenous (IV) products,instruments, surgical supplies, sutures, syringes, needles,pharmaceuticals, housekeeping supplies, laboratory supplies, etc.). Thereport may be presented on the display(s) 112 (e.g., user interface),stored and/or transmitted to another device.

The report may be organized and/or filtered to illustrate comparisons ofoutcomes, costs and/or supply utilization with respect to one or morehealth care systems (e.g., a group of hospitals, clinics, surgicalsuites, offices, etc.), one or more sites (e.g., hospital(s)) and/or oneor more health care providers (e.g., physician(s), surgeon(s),physician's assistant(s), nurse(s), etc.). For example, a report mayillustrate a comparison of outcomes (e.g., readmissions within 30 days)between hospitals (or health care providers) for a health care cohort.For example, a report may illustrate a comparison of outcomes (e.g.,readmissions within 30 days) between hospitals (or health careproviders) for a health care cohort (e.g., laparoscopic appendectomy).Additionally or alternatively, a report may illustrate a comparison ofcosts between hospitals (or health care providers) for a health carecohort. Additionally or alternatively, a report may illustrate acomparison of supply utilization (e.g., number of bandages perencounter, etc.) between hospitals (or health care providers) for ahealth care cohort.

In some configurations, the report may be a site-specific report. Forexample, the report may illustrate metrics (e.g., outcomes, costs,supply utilization, etc.) that are specific to a site (e.g., hospital,surgical suite, clinic, etc.). For instance, the report may comparemetrics between health care providers at a hospital. In someconfigurations, the report may be a health care provider-specificreport. For example, the report may illustrate metrics (e.g., outcomes,costs, supply utilization, etc.) that are specific to a health careprovider (e.g., hospital, surgical suite, clinic, etc.). For instance,the report may illustrate metrics for a health care provider over anumber of encounters.

In some configurations, the report may be organized to prioritizepotential for improvement. For example, a list of sites and/or healthcare providers in the report may be sorted such that the metric (e.g.,readmission rate, cost per encounter, supply utilization, etc.) for ahealth care cohort proceeds from the highest in descending order.Additionally or alternatively, a report may illustrate the comparativemetrics (e.g., best and/or average outcomes, lowest and/or average costper encounter, most efficient and/or average supply utilization, etc.)in comparison with the metrics of one or more health care systems,sites, and/or health care providers. In some approaches, the report mayprovide additional detail associated with the comparative metrics. Forexample, the report may illustrate the types and/or numbers of suppliesused in a best and/or average encounter scenario. In another example,the report may illustrate one or more reasons for readmission of one ormore sites and/or health care providers in comparison with the reason(s)for readmission for a site and/or health care provider.

In some configurations, the report may include a trend graph. The trendgraph may illustrate the cost trend over time for a specific health carecohort for a health care system, a site (e.g., hospital, clinic, etc.)and/or a health care provider.

In some configurations, the report may be interactive. For example, thereport may be presented on the user interface 114. The user interface114 may include controls for changing the organization and/or scope of areport. For example, the user interface 114 may receive one or moreinputs that specify a selection of a specific health care intervention(e.g., health care cohort), a scope (e.g., site comparison, health careprovider comparison, etc.), one or more metrics (e.g., outcomes, costs,supply utilization, etc.), a metric sorting (e.g., highest to lowest,lowest to highest, date, etc.), etc. In some configurations, the reportmay include one or more controls for selecting one or more alternativesupplies. For example, the report may include a list of supplies for aparticular health care cohort used by a health care system, site, and/orhealth care provider. The computing device 102 (e.g., report generator120, user interface 114, etc.) may produce the control (e.g., drop downmenu, list, radio buttons, etc.) which may illustrate a selection of oneor more alternative supplies (and their associated cost(s), forexample). In some configurations, the control may emphasize (e.g.,highlight, illustrate in bold, italics, different font size, differentcolor, etc.) a cheapest alternative supply and/or an alternative supplyutilized in a comparative scenario (e.g., by a different health careprovider, in a standard encounter, in a lowest cost encounter, etc.).This approach may illustrate supply changes that may reduce cost for aspecific health care intervention. In some configurations, the reportmay include an inventory listing of one or more types of supplies. Forexample, the inventory listing may indicate one or more numbers of oneor more types of supplies that are on hand (at one or more sites,hospitals, clinics, etc., for instance).

It should be noted that one or more of the elements or components of thecomputing device 102 may be combined and/or divided. For example, thehealth care cohort identifier 116, the cohort creator 118, and/or thereport generator 120 may be combined. Additionally or alternatively, oneor more of the health care cohort identifier 116, the cohort creator118, and/or the report generator 120 may be divided into elements orcomponents that perform a subset of the operations thereof.

FIG. 2 is a flow diagram illustrating one configuration of a method 200for generating a report. The method 200 may be performed by thecomputing device 102 described in connection with FIG. 1.

The computing device 102 may identify 202 one or more specific healthcare interventions. This may be accomplished as described in connectionwith one or more of FIGS. 1 and 3-4. For example, the computing device102 may receive an input that specifies a specific health careintervention, may determine a primary intervention code that covers thespecific health care intervention and/or may perform cost and/orencounter analysis in order to identify the specific health careintervention (e.g., to generate a suggested health care cohort).

The computing device 102 may create 204 one or more health care cohortsfor the one or more specific health care interventions. This may beaccomplished as described in connection with one or more of FIGS. 1 and4-8, 10-11 and 13-14. For example, the computing device 102 maydetermine one or more encounters that are consistent with the healthcare cohort (e.g., consistent with the primary intervention, acceptablesecondary intervention(s) and/or exclusionary secondary intervention(s),etc.). In some configurations, creating 204 the one or more health carecohorts may include extracting data, organizing the health care cohortand/or cleaning the health care cohort.

The computing device 102 may generate 206 a report based on the healthcare cohort. This may be accomplished as described in connection withone or more of FIGS. 1, 4, 9-10 and 12. For example, the computingdevice 102 may generate one or more reports that illustrate one or morehealth care intervention metrics (e.g., outcomes, costs, and/or supplyutilization). Generating 206 the report(s) may include organizing thereport(s) with respect to one or more health care systems, one or moresites and/or one or more health care providers. Additionally oralternatively, generating 206 the report(s) may include illustratingcomparisons (of outcomes, costs and/or supply utilization, for example)based on one or more health care cohorts.

FIG. 3 is a block diagram illustrating an example of a health carecohort identifier 316. The health care cohort identifier 316 may be oneexample of the health care cohort identifier 116 described in connectionwith FIG. 1. In this example, the health care cohort identifier 316 maydetermine a suggested health care cohort 330. One example of an analysisthat may utilize a health care cohort is a hospital seeking reportscomparing the cost of one intervention in a year to the cost of theintervention from the previous year. If there is significant variationin the cost of an intervention from one time period to another, ahospital or health care provider team may look for ways to reconcilethose cost differences and/or modify future decision making to betterpredict costs for patients and meet internal budget goals.

In some approaches, the health care cohort identifier 316 may receiveinput 332 from one or more users (e.g., a data management team and/or ateam of health care providers). For example, reception of the input 332may initiate the identification of a health care cohort. The input 332may indicate a selected health care cohort and/or a selected health careintervention group. For instance, the health care cohort identifier 316may select cost- and/or encounter-related analytics based on one or moreexisting health care intervention groups (e.g., groups of interventionsbased on one or more criteria, such as an ICD code and/or CPT code).Analytic considerations in this process may include, for example,searching one or more databases to find costs beyond a threshold, suchas the opportunity from the total dollar amount above average+1 SD (morethan one standard deviation) 324 or opportunity from the proportion ofencounters more than 1 SD from the average 322. Further analytics mayinclude opportunity from the coefficient of variation 326 andopportunity from the contribution to the margin 328, as illustrated inFIG. 3. For example, an “opportunity” may indicate health care cohort(s)with encounters with a given magnitude of cost difference amongst theencounters in the health care cohort.

One approach for developing a new health care cohort may involve ananalytic review of a period of (e.g., the past several years') completedencounters for a selected health care intervention. The group ofcompleted encounters may represent a group of patients, interventionsand/or encounters that may be beneficial to analyze, obtain furtherdetail on and/or form a health care cohort with. The health care cohortidentifier 316 may create a set (e.g., list) of all additionalinterventions (e.g., secondary interventions) performed with theselected health care intervention. In some approaches, the health carecohort identifier 316 may present the set (e.g., list). For example, theset may be reviewed by a health care provider. The health care cohortidentifier 316 may receive input 332, which may indicate which of thosesecondary interventions is considered appropriate to associate with theprimary intervention. In some examples, developing a new health careintervention group may be initiated by reviewing encounters associatedwith a particular intervention that uses particular implants.Accordingly, the health care cohort identifier 316 may produce asuggested health care cohort 330 (e.g., a health care prehort) based onthe one or more analytic considerations.

FIG. 4 is a flow diagram illustrating a more specific configuration of amethod 400 for generating a report. For example, the method 400 mayinclude processing and refining health care intervention comparison dataand generating a detailed analytic report. The method 400 may beperformed by the computing device 102 described in connection withFIG. 1. The method 400 may be a more specific configuration of themethod 200 described in connection with FIG. 2.

The computing device 102 may identify 402 a specific health care cohort.This may be accomplished as described in connection with one or more ofFIGS. 1-3. For example, the computing device 102 may determine asuggested health care cohort (e.g., a health care prehort).

Once a suggested health care cohort has been obtained (as described inconnection with FIG. 3, for example), the computing device 102 mayextract 404 data (e.g., health care cohort data) from one or moredatabases (e.g., an EDW). This may be accomplished as described inconnection with one or more of FIGS. 1 and 5. It should be noted that anEDW or a data warehouse (DW) may be utilized for reporting and dataanalysis. DWs may be central repositories of integrated data from one ormore disparate sources. EDWs may store current and historical data andmay be utilized for creating trending reports for senior managementreporting such as annual and quarterly comparisons. The extracted datamay provide definitions of populations that underwent the selectedhealth care intervention, including an age range, gender and/or healthcomplications, etc.

Once the population data has been extracted 404, the computing device102 may present 406 the data for review. This may be accomplished asdescribed in connection with one or more of FIGS. 1 and 5. For example,the computing device 102 may present the data on the display(s) 112 forreview.

The computing device 102 may determine 408 whether the health carecohort is verified. This may be accomplished as described in connectionwith one or more of FIGS. 1 and 5. For example, the computing device 102may determine whether any change input has been received. For instance,one or more users (e.g., data management team and/or health careprovider(s), nurse, etc.) may review the data (e.g., health care prehortdata) to see if the health care cohort and/or extracted data accuratelyreflect the specific health care cohort. In other words, the health carecohort may be verified in a situation that the data reflects a group ofencounters for the specific health care cohort. In the situation thatthe computing device 102 receives one or more change inputs (e.g., thehealth care cohort is not verified) the computing device 102 mayredefine and reorganize 410 the data and repeat presenting 406 the datafor review. The computing device 102 may repeat determining 408 whetherthe health care cohort is verified. The computing device 102 maycontinue iterating these operations until the data reflects an accurategroup of encounters for the specific health care cohort.

In a situation that the health care cohort is verified, the computingdevice 102 may clean 412 the health care cohort data. For example, thecomputing device 102 may clean and analyze the data and/or identify anydata quality issues. Cleaning the data may include resolving any dataquality issue(s) (e.g., fixing data errors) that may have occurred inthe recording of the data. Examples of possible data errors or dataquality issues are further described in connection with FIGS. 7 and 8.Examples may include, but are not limited to, the use of data fromjunked accounts or reused accounts, incomplete Medicare encounters,invalid costs, encounter dates that precede their admit date, wrongprimary care provider assigned to an encounter, etc. Junked accounts maybe remnants of joining the information together from two accounts (thissometimes happens with Medicare Patients, incapacitated patients, etc.).

The computing device 102 may populate 414 the health care cohort tablesin one or more databases (e.g., one or more EDW tables) (once the healthcare cohort data has been cleaned 412, analyzed and/or any data qualityissues have been identified, for example). This may be accomplished asdescribed in connection with FIG. 1. For example, the computing device102 store one or more encounters corresponding to the health care cohortin the EDW.

The computing device 102 may analyze 416 the data from the health carecohort tables. For example, the computing device 102 may attempt toidentify any remaining data quality issues from the health care cohorttables. For instance, analyzing 416 the health care cohort table datamay be performed similarly to the data analysis described above for thehealth care prehort data. In some configurations, the computing device102 may present the health care cohort table data for review. In theseconfigurations, the computing device 102 may receive one or more changeinputs and/or one or more input indications of data quality issues.Accordingly, the computing device 102 may analyze the health care cohorttable data automatically and/or optionally may receive input thatindicates one or more data quality issues.

The computing device 102 may determine 418 whether any data qualityissue(s) remain. For example, in a situation that analyzing 416 thehealth care cohort table data indicates one or more data quality issues,the computing device 102 may identify 420 issue-causing data. Forexample, the computing device 102 may mark (e.g., tag, label, record,etc.) the data in the health care cohort table data that is causing thedata quality issue(s). The computing device 102 may repeat cleaning 412the health care cohort data. For example, the computing device 102 mayresolve the identified data quality issue and/or remove theissue-causing data (e.g., encounter).

In a situation that there are no remaining data quality issues, thecomputing device 102 may generate 422 a report (e.g., an analytic report422). This may be accomplished as described above in connection with oneor more of FIGS. 1-2.

FIG. 5 is a flow diagram illustrating one configuration of a method 500for extracting health care cohort data. The method 500 may be performedby the computing device 102 described in connection with FIG. 1. Forexample, one or more of the operations described in connection with FIG.5 may be performed by the cohort creator 118 described in connectionwith FIG. 1. Additionally or alternatively, one or more of theoperations (e.g., steps) of the method 500 may be performed as part ofand/or in conjunction with extracting 404 the data and/or presenting 406the data for review as described in connection with FIG. 4.

The computing device 102 may perform 502 a preliminary extraction fromone or more databases. For example, the computing device 102 may perform502 a preliminary extraction of data (e.g., encounters) with the primaryintervention code from the EDW. As described above, the primaryintervention code may be received by the computing device 102 (as inputfrom a user, for example) and/or may be identified by the computingdevice 102. The primary intervention code may be coding terminology(e.g., ICD code, CPT code, etc.) used by one or more national healthcare registries. One or more parameters (e.g., code(s), database(s),data range(s), etc.) may be utilized in performing 502 the preliminaryextraction. For example, performing 502 the preliminary extraction maybe performed 502 with one or more codes and one or more databases (e.g.,general hospital billing intervention codes from a patient registrydatabase). In some approaches, one or more parameters for performing 502the preliminary extraction may be received as input (from a user, healthcare provider, etc., for example). The preliminary extraction may berefined and/or constrained by one or more date ranges. For example, onlydata (e.g., health care interventions, encounters, etc.) completed sincea specific date (e.g., Jan. 1, 2008, etc.) may be extracted.

In some configurations, the computing device 102 may identify 504 afrequency of encounters with the primary intervention code. For example,the computing device 102 may determine how often (e.g., number ofencounters over a period of time) encounters with the primaryintervention code tend to occur. It should be noted that erroneouschanges may be made in health care information systems that lead to dataquality issues. For example, if the hospital has a frequency 100appendectomy surgeries a month for the last 12 months, and then the next3 months the frequencies for appendectomies go to zero, this is morelikely related to a data quality issue than the hospital really notproviding any appendectomy surgeries.

In some configurations, the computing device 102 may identify 506secondary interventions completed with the primary intervention. Forexample, the computing device 102 may determine one or more secondaryinterventions (e.g., general billing codes) that occur in the sameencounters as the primary intervention.

In some configurations, the computing device 102 may identify 508 afrequency of secondary interventions. For example, the computing device102 may determine how often (e.g., number of encounters over a period oftime, a number of encounters out of the encounters with the primaryintervention over the total number of encounters with the primaryintervention, etc.) encounters with the secondary intervention(s) tendto occur.

In some configurations, the computing device 102 may present 510 thedata for review. This may be accomplished as described in connectionwith one or more of FIGS. 1 and 4. For example, the preliminary dataextracted and/or accompanying data (e.g., frequency of encounters withthe primary intervention code, secondary interventions, frequency ofsecondary interventions, etc.) may be presented (e.g., presented on adisplay, printed, transmitted, etc.). As described in connection withFIG. 4, the computing device 102 may verify the health care cohort(e.g., health care prehort). The computing device 102 may receive one ormore change inputs (e.g., in an encounter that a health care providerwants to remove one or more factors and/or add one or more factors todescribe the health care cohort population). As described in connectionwith FIG. 4, the data may be redefined and/or reorganized based on oneor more change inputs. The health care cohort (e.g., prehort) may beverified (e.g., no change inputs are received and/or approval isreceived). For example, a health care provider may not have any changeswhen the health care cohort (e.g., prehort) is representative of thedesired health care cohort or specific health care intervention. In someconfigurations, the data may then be organized, as described inconnection with one or more of FIGS. 1, 4 and 6.

FIG. 6 is a flow diagram illustrating one configuration of a method 600for organizing a health care cohort. The method 600 may be performed bythe computing device 102 described in connection with FIG. 1. Forexample, the method 600 may be performed as described in connection withFIG. 1. Organizing the health care cohort may include defining and/orredefine the health care cohort. As described herein, a health carecohort may offer more specificity than general health care interventioncodes. For example, one health care intervention code may correspond toall appendectomies, including robotic appendectomies, laparoscopicappendectomies and open surgery appendectomies. For comparing costs,this data may not be accurately comparable since a robotic interventionmay be significantly more expensive than the other types ofinterventions.

To organize a health care cohort, the parameters may be organized in oneor more databases for a health care cohort. For example, the parametersmay be organized into three categories: primary intervention definitionparameters (e.g., mandatory code types), acceptable secondaryintervention parameters (e.g., allowable code types), and/orexclusionary secondary intervention parameters (e.g., exclusionary codetypes). Organizing the health care cohort may be performed by thecomputing device 102 described in connection with FIG. 1. For instance,if an intervention is coded with a certain ICD procedure code, one ormore rules (e.g., logic) may be utilized by the computing device 102 toinclude that encounter in a health care cohort. Many different rules(e.g., types of logic) may be utilized. In some configurations, thecomputing device 102 may receive input to add, remove and/or modify oneor more parameters. The aforementioned three categories may include thesame and/or different parameters. Some examples of parameters (e.g.,intervention codes) that may be included in one or more of thecategories may include one or more of the following parameters.Procedure codes may include International Classification of Diseases(ICD)-9 procedure codes, ICD-10 procedure codes and/or Current ProcedureTerminology (CPT) codes, etc. Diagnosis codes may include ICD-9diagnosis codes, ICD-10 diagnosis codes, and/or systemized nomenclatureof medicine (SNOMED) Clinical Terms (CT), etc. SNOMED may be a standard(similar to ICD-10, for example), but may be specifically used bypathologist(s). A hospital information system intervention procedurecode may be another example of a parameter. Billing groups may includeinternal charge codes, Diagnosis Related Groups (DRGs), All PatientRefined Diagnosis Related Groups (APRDRGs) and/or Medicare SeverityDiagnosis Related Groups (MSDRGs), etc. Item types may includeunspecified codes, explicitly state codes, similarly sounding itemsand/or item descriptions, etc. For example, item types may include codessuch as United Nations Standards Products and Services Code (UNSPSC)codes, item description and/or internal classifications, etc. Registriesmay include the Society of Thoracic Surgery (STS) database, NationalSurgery Quality Improvement Project (NSQIP) and/or internal diseasespecific registries, etc. Comorbidity indices may include examples suchas Charleson Comorbidity Index, Severity of Illness (SOI) and/or Risk ofMortality (ROM), etc. Provider specialty may be a health care providerspecialty (e.g., physician specialty). Hospital descriptors may behospital assigned codes including trauma level, patient status toinclude inpatient and outpatient and/or operating room level, etc. Thenumber of surgeries may be the number of times a patient visited theoperating room during a patient encounter. Patient demographics mayinclude age, body mass index (BMI), weight, and gender. Clinical notesmay include notes related to operative summary, discharge, admit,history and/or physical, etc. Clinical test results examples may includevital signs, pathology, laboratory results, and/or imaging results, etc.

It should be noted that fewer, more and/or alternative parameters may beutilized in some configurations. For example, any data element storedwithin an electronic medical record (EMR) may be utilized. It should benoted that utilizing many different coding terminologies from variousinstitutions and health care providers may allow creating more specific,accurate, and/or homogenous health care cohorts for comparison thanexisting analytic products on the market.

The computing device 102 may identify 602 one or more parameters for theprimary intervention. For example, the computing device 102 associateand/or store one or more parameters for the primary interventiondefinition. For instance, the computing device 102 may store an ICD codeand a CPT code that specify a spinal fusion, a charge code thatindicates a 2 level spinal fusion, an item description for a titaniumplate used in 2 level spinal fusions and clinical note keywords (e.g.,“2 level,” “L2/L3,” etc.) that may indicate a two level spinal fusion.

The computing device 102 may identify 604 one or more parameters for oneor more acceptable secondary interventions. For example, the computingdevice 102 associate and/or store one or more parameters for anyacceptable secondary intervention(s). For instance, the computing device102 may store an ICD code specifies spinal stenosis and a CPT code thatspecifies laminectomy.

The computing device 102 may identify 606 one or more parameters for theprimary intervention. For example, the computing device 102 associateand/or store one or more parameters for one or more exclusionarysecondary interventions. For instance, the computing device 102 maystore a charge code that indicates a 3 level spinal fusion, an itemdescription for a titanium plate used in 3 level spinal fusions andclinical note keywords (e.g., “3 level,” “L2/L3/L4,” etc.) that mayindicate spinal fusions other than 2 level spinal fusions.

The computing device 102 may formalize the health care cohort. This maybe accomplished as described above in connection with FIG. 1. Forexample, the computing device 102 may indicate that the health carecohort is formalized (e.g., that the health care cohort is no longer ahealth care prehort).

The computing device 102 may present 610 data for review. This may beaccomplished as described in connection with one or more of FIGS. 1 and4. For example, the computing device 102 may present the organizedhealth care cohort data on one or more displays 112 (e.g., on a userinterface 114), may print the health care cohort data and/or may sendthe health care cohort data. As described herein, the computing device102 may reorganize and/or redefine the health care cohort (e.g.,parameters) if one or more change inputs are received. Alternatively,the computing device 102 may further use the health care cohort if nochange inputs are received and/or if an input indicating health carecohort approval is received.

FIG. 7 is a flow diagram illustrating one configuration of a method 700for assigning a primary health care provider (e.g., correct primaryhealth care provider). For example, the method 700 may be performed asan example of and/or part of cleaning the health care cohort data asdescribed in connection with one or more of FIGS. 1 and 4. In otherwords, the method 700 may be an example of analyzing data, identifyingone or more data quality issues and/or resolving the one or more dataquality issues. As described above, health care cohort data may containerrors, such as mistakes in the input and recording of the data. Themethod 700 may be performed in order to assign a correct primary healthcare provider. For example, a wrong surgeon may be associated with aparticular encounter. For instance, if a patient had heart surgery, themethod 700 may help to ensure that the surgeon assigned to the encounterhad the specialty of cardio thoracic surgeon. If the associated surgeonfor the cardiac encounter was a urologist, the encounter may not beaccepted into the health care cohort.

In some approaches, cleaning health care cohort data may includeidentifying how many health care providers have a certain specialty inorder to determine whether the correct primary provider is recorded foran encounter (e.g., health care intervention and/or patient). Forexample, data quality issues may be identified by determining whether ahealth care provider listed as a primary (e.g., primary provider) inhospital billing records has an appropriate specialty (for an encounterand/or a health care intervention, for instance). The data may berejected if the health care provider does not have the appropriatespecialty.

Data relating to the assignment of a health care provider may need to becorrected if errors or data quality issues are discovered. Potentialdata quality issues may include, for example, a clerical error such as awrong health care provider having been assigned to an encounter, or twohealth care providers with the same name but different specialties beingmistaken in the health care provider assigning process. Some data mayneed to be removed from the health care cohort population if an error isidentified and rejected in this process.

The method 700 described in connection with FIG. 7 may be performed bythe computing device 102 described in connection with FIG. 1, forexample. The computing device 102 may determine 702 how many providers(e.g., primary providers) have an appropriate specialty (for a specifichealth care intervention and/or for the health care cohort, forinstance). For example, the computing device 102 may identify one ormore appropriate specialties for performing a specific health careintervention. In some approaches, the computing device 102 may requestand/or receive the appropriate specialty(ies) for performing thespecific health care intervention. For example, the computing device 102may receive one or more appropriate specialties from one or moredatabases (e.g., an EDW) and/or from input. An appropriate specialty isa specialty that is qualified to perform the specific health careintervention. For example, a health care provider with a neurosurgeryspecialty may be qualified to remove a brain tumor but a health careprovider with a podiatry specialty may not be qualified to remove abrain tumor. The computing device 102 may determine 702 how many (e.g.,a number of) primary providers that have one or more of the appropriatespecialties. For example, the computing device 102 may determine thenumber of primary health care providers (for an encounter, for example)that have an appropriate specialty for performing the specific healthcare intervention.

In a situation that zero primary providers have the appropriatespecialty, the computing device 102 may determine 704 whether theprovider listed as a primary in hospital billing records has anappropriate specialty. For example, the computing device 102 may look upthe specialty(ies) of the health care provider listed as a primaryhealth care provider in the hospital billing records (in one or moredatabases, for example). If the specialty of the primary health careprovider matches the appropriate specialty (e.g., at least one of theappropriate specialties), the computing device 102 may use 706 thatprovider (e.g., may associate that provider with the encounter in thehealth care cohort).

If the provider listed as the primary in hospital billing records doesnot have an appropriate specialty, the computing device 102 maydetermine 708 whether the provider listed as attending in hospitalbilling records has an appropriate specialty. For example, the computingdevice 102 may look up the specialty(ies) of the health care providerlisted as an attending health care provider in the hospital billingrecords (in one or more databases, for example). If the specialty of theattending health care provider matches the appropriate specialty (e.g.,at least one of the appropriate specialties), the computing device 102may use 710 that provider (e.g., may associate that provider with theencounter in the health care cohort). If the specialty of the attendinghealth care provider does not match the appropriate specialty (e.g., anyof the appropriate specialties), the computing device 102 may reject 712the data. For example, the computing device 102 may remove the encounterfrom the health care cohort.

In a situation that one provider has the appropriate specialty, thecomputing device 102 may use 724 that provider. For example, thecomputing device 102 may associate that provider with the encounter inthe health care cohort.

In a situation that two or more providers have the appropriatespecialty, the computing device 102 may determine 714 whether oneprovider with an appropriate specialty is listed as a primary inhospital billing records. For example, the computing device 102 may lookup the specialty(ies) of the health care providers listed as primaryhealth care providers in the hospital billing records (in one or moredatabases, for example). If the specialty of one (e.g., only one) of theprimary health care providers matches the appropriate specialty (e.g.,at least one of the appropriate specialties), the computing device 102may use 716 that provider (e.g., may associate that provider with theencounter in the health care cohort).

If the providers listed as primary in hospital billing records do nothave an appropriate specialty, the computing device 102 may determine718 whether one provider with an appropriate specialty is listed asattending in hospital billing records. For example, the computing device102 may look up the specialty(ies) of the health care provider(s) listedas an attending health care provider in the hospital billing records (inone or more databases, for example). If the specialty of the attendinghealth care provider matches the specialty (e.g., at least one of theappropriate specialties), the computing device 102 may use 720 thatprovider (e.g., may associate that provider with the encounter in thehealth care cohort). If the specialty of the attending health careprovider(s) does not match the specialty (e.g., any of the appropriatespecialties), the computing device 102 may reject 722 the data. Forexample, the computing device 102 may remove the encounter from thehealth care cohort.

FIG. 8 is a flow diagram illustrating a configuration of a method 800for cleaning data (e.g., actively monitoring for data inconsistencies toidentify and take corrective action). In some approaches, some dataquality issues may be sent for correction. For example, different dataquality issues may originate from different sources. The data may besent to another system for review and resolution.

One or more of the operations of the method 800 may be performed by thecomputing device 102 described in connection with FIG. 1. Additionallyor alternatively, one or more of the operations of the method 800 may beperformed by another system (e.g., a remote electronic device fromanother department and/or entity of a health care system). Some hospitalbilling errors may include examples such as junked accounts, reusedaccounts, incomplete Medicare encounters (e.g., I42s), merged EnterpriseMaster Patient Index (EMPI), and a discharge date before the admit date.The computing device 102 may detect 802 one or more hospital billingerrors. The computing device 102 may send 804 the hospital billingerrors. For example, the hospital billing errors may be sent to ahospital billing system (e.g., health information management system).One or more corrections may be applied 806 to the health informationmanagement system. Corresponding changes may be applied 826 to the EDW.

Financial billing errors may include errors such as invalid costs andmay be resolved by the hospital financial system. For example, aninvalid cost may occur when the hospital cost for the encounter is morethan the hospital charge for the encounter. This may typically representa data error. The computing device 102 may detect 808 one or morefinancial billing errors. The computing device 102 may send 810 thefinancial billing errors. For example, the financial billing errors maybe sent to a finance system. One or more corrections may be applied 812to the hospital financial system. Corresponding changes may be applied826 to the EDW.

Clinical documentation errors may include examples such as the accountnumber not joining to the electronic medical record, encounter datebefore admit date, encounter date before discharge and wrong specialtyfor provider, etc. An encounter may be a record with a unique numberassigned to a patient when they have an interaction (e.g., hospitalstay) with a health care provider and/or facility. An encounter may helptrack all the activities that occurred to the patient during their stay.For example, health care data may be stored in different databases.Radiology may have a radiology database that is separate from alaboratory database that houses laboratory data. In some instances,these databases may get out of sync. For example, Patient A may haveencounter 12345 in the radiology database, but the same Patient A hasencounter number 5678 in the laboratory database. The databases shouldhave the same encounter number for Patient A, but have differentencounter numbers. Accordingly, when attempting to determine a patient'stotal cost for a hospital stay, the radiology data may not join with thelaboratory data. Accordingly, the two medical records may not be joinedbecause of the encounter number differences. Corrections may be madewithin the electronic medical record. The computing device 102 maydetect 814 one or more clinical documentation errors. The computingdevice 102 may send 804 the clinical documentation errors. For example,the clinical documentation errors may be sent to a clinicaldocumentation steward system. One or more corrections may be applied 818to the electronic medical record. Corresponding changes may be applied826 to the EDW.

Hospital inventory errors may include no material management. Inventorydata may be corrected by the inventory management system (e.g.,purchasing department). The computing device 102 may detect 820 one ormore hospital inventory errors. For example, hospital inventory errorsmay be detected 820 in material management data. Material managementdata may include supplies and/or items. For instance, materialmanagement data may be stored in a specific database that deals with thesupplies or items that are used for the patient care during theirhospital stay (e.g., scalpels, oxygen tubes, sheets, gowns, gloves,etc.). The computing device 102 may send 822 the hospital inventoryerrors. For example, the hospital inventory errors may be sent to asupply chain system. One or more corrections may be applied 824 to thesupply chain system (e.g., inventory management system). Correspondingchanges may be applied 826 to the EDW.

In addition to or alternatively from other systems correcting errors,the computing device 102 may clean the data (e.g., correct one or moreerrors) using a specific code or algorithm. One example is given inconnection with FIG. 7. Additionally or alternatively, the EDW may cleandata and/or reorganize the data using a specific code or algorithm. Forexample, the EDW may execute logic in order to collect data, clean dataand/or organize the data into one or more health care cohorts. Thealgorithm may include one or more processes described herein forobtaining data (e.g., encounter data, etc.) and/or organizing the datafor reporting.

The computing device 102 may monitor 828 data trends. For example, thecomputing device 102 (e.g., report steward) may perform ongoing datatrend surveillance for the appropriateness of the errors being sent forresolution. For example, generating reports may depend on a flow of datafrom one or more data sources. In some instances, something may changein a data source to cause the data to stop flowing. For example, ahealth care cohort may be defined by an ICD procedure code andcorresponding data may populate one or more database tables. Thecomputing device 102 may detect (upon a certain date, after a length oftime, for example) that new records for health care cohorts defined byan ICD code has not been received. The computing device 102 may indicatethat something has changed that prevents one or more health care cohortsfrom being updated. For example, one cause of such a problem may be thatan intervention is no longer coded with ICD-9 codes, but with ICD-10codes instead. In some configurations, the computing device 102 mayupdate the definition of one or more health care cohorts to includeICD-10 codes. Other systems (e.g., hospitals) may identify differenttypes of data errors in some approaches.

The computing device 102 may populate 830 the EDW tables (with thecorrected EDW data, for example). For example, once the corrections havebeen applied to the EDW, the data can be used to populate the healthcare cohort tables in the EDW in order to generate more accuratereports. The computing device 102 may generate 832 one or more reports(e.g., analytic reports). This may be accomplished as described inconnection with one or more of FIGS. 1-2, 4, 9-10 and 12.

FIG. 9 is a diagram illustrating a method 900 for analyzing data trendsin database (e.g., EDW) tables. The method 900 may be one example of theprocess of analyzing 416 data described in connection with FIG. 4. Themethod 900 may be performed by the computing device 102 and/or one ormore electronic devices 110 described in connection with FIG. 1. Themethod 900 may enable monitoring data for issues that may qualify anencounter for multiple health care cohorts. For example, the method 900may include identifying and/or resolving one or more issues. In someconfigurations, identified issue(s) may be presented to a user (e.g., adata management team).

The computing device 102 may access 902 the data in the Enterprise DataWarehouse (EDW) tables. For example, the computing device 102 may access(e.g., search, request, retrieve, etc.) data from one or more EDWtables. The data may indicate one or more encounters. An encounter maybe a record (with a unique number, for example) of an interactionbetween a health care provider and a patient.

The computing device may evaluate 904 encounters that qualify formultiple health care cohorts. For example, the computing device 102 maydetermine whether any encounters are included in multiple health carecohorts. For instance, the assignment of primary and secondaryinterventions for an encounter may have errors that allow an encounterto qualify for multiple health care cohorts. In some instances, twohealth care cohort definitions may be similar enough that a patient'sencounter can meet the criteria to be in both health care cohorts. Insome configurations, encounters may be sent to an appropriate datasteward system. For example, a data steward system may be a system thatcollects and processes data. In some configurations, the data stewardmay present encounter data to one or more users for review. Additionallyor alternatively, the data steward system may resolve one or more issuesand/or receive input with corrections for resolving one or more issues.

The computing device 102 may evaluate 906 encounter volume trends. Forexample, the computing device 102 may determine how often a health careintervention is being performed over time and/or may determine whetherthe health care intervention is being performed with or without one ormore additional interventions. For instance, ongoing surveillanceevaluates encounter volume trends, such as if an intervention is beingdone less frequently or now is done with or without an additionalintervention. In some configurations, evaluating 906 encounter volumetrends may include assessing for inconsistencies within health carecohorts and/or health care exhorts, by facility, by primary surgeon, bymonth and/or by year.

The computing device 102 may evaluate 908 medical supply inconsistenciesrelated to health care cohort definitions. For example, the computingdevice 102 may determine when a particular medical supply may be missingfrom an encounter.

The computing device 102 may evaluate 910 values related to health careencounter(s). For example, the computing device 102 may evaluate valuesfor cost and/or logged minutes related to the health care encounter(s)(e.g., health care cohort). For example, the computing device 102 maymonitor for changes occurring to one or more health care cohorts. Forinstance, if over the last 5 years the average operating room (OR) timefor appendectomies is 100 minutes and then in the last two month theaverage OR time doubles to 200 minutes, the computing device 102 mayprovide an indication that an investigation of the sudden increase maybe beneficial.

The computing device 102 may evaluate 912 encounter volumes gettingassigned to reject bins. For example, the computing device 102 maydetermine encounters that are getting assigned to reject bins toidentify data trends.

The computing device 102 may determine 914 whether any data qualityissues were identified. For example, the computing device 102 maydetermine 914 whether any of the evaluations 904, 906, 908, 910, 912identified any issues.

If the computing device 102 determines 914 that one or more data qualityissues were identified, the computing device 102 may assign 916 theissue(s) to an appropriate data management source for resolution. Forexample, the computing device 102 may send (e.g., return) one or moreencounters for resolution by the billing and/or clinical documentationsteward system(s).

The computing device 102 may resolve 918 the one or more issues. Forexample, the computing device 102 may make one or more automaticcorrections, may make one or more corrections based on an input and/ormay receive one or more corrections. The computing device 102 maygenerate 920 one or more reports (based on the corrected data, forexample). This may be accomplished as described in connection with oneor more of FIGS. 1-2, 4, 8, 10, 12 and 14. It should be noted that ifany data quality issues are identified in this analysis of data in theEDW tables, one or more processes of the method 900 of resolving theissues may repeat by cleaning and analyzing the data (as described inconnection with one or more of FIGS. 1-2, 4 and 7-8, for example) If nodata quality issues are identified, the computing device 102 maygenerate 920 reports.

FIG. 10 is a flow diagram that illustrates a more specific configurationof a method 1000 for analyzing data to determine data quality issues(e.g., analyzing encounters). The method 1000 may be performed by thecomputing device 102 and/or one or more electronic devices 110 describedin connection with FIG. 1. The computing device 102 may identify 1002 anencounter (e.g., medical encounter). For example, the computing device102 may access the EDW to identify an encounter.

The computing device 102 may determine 1004 whether the encounter data(e.g., data corresponding to the encounter) fits the health care cohortdefinition. For example, the computing device 102 may determine whetherthe encounter data indicates a primary intervention, one or moreacceptable secondary interventions, and/or one or more exclusionarysecondary interventions.

If the computing device 102 determines 1004 that the encounter fits thedefinition of a health care cohort, the computing device 102 may assign1006 the encounter to a health care cohort. For example, the computingdevice 102 may add the encounter to the health care cohort (e.g., storethe encounter in a health care cohort table).

The computing device 102 may determine 1008 whether the encounter hasdata quality issues. For example, it may be beneficial to determine ifthe encounter fits into the health care cohort but still has dataquality issues. This may be accomplished as described in connection withone or more of FIGS. 1-2, 4 and 7-9, for instance. If the encounter doesnot have data quality issues, the computing device 102 may assign 1010the encounter to the health care cohort. For example, the computingdevice 102 may store the encounter in a health care cohort table. Thecomputing device 102 may generate 1012 one or more reports (e.g.,analytic reports) based on the health care cohort. This may beaccomplished as described in connection with one or more of FIGS. 1-2,4, 8-9, 12 and 14, for instance. For example, if the encounter does nothave data quality issues, the encounter may be assigned 1010 to thehealth care cohort and included within an analytic report.

If the computing device 102 determines 1008 that the encounter fits adefinition of a health care cohort but has quality issues, the computingdevice 102 may reject 1022 the encounter (because the data needs to becleaned, for example) and/or may reject 1024 the health care cohort. Forexample, the computing device 102 may not utilize the encounter as validdata. The computing device 102 may produce 1026 a data issues report.For example, the computing device 102 may generate a report thatindicates one or more encounters, health care cohorts, health careexhorts, and/or general billing groups with data quality issues.

If the computing device 102 determines 1004 that the encounter does notfit the definition of a health care cohort, the computing device 102 maydetermine 1014 whether the encounter needs further clinical review. Forexample, the computing device 102 may determine whether the encountermeets criteria for inclusion in a defined health care cohort or needs tobe flagged for further review. Additionally or alternatively, thecomputing device 102 may make this determination 1014 based on one ormore rules (e.g., flag all encounters that do not fit a health carecohort definition, flag non-health care cohort encounters from aparticular clinic/health care provider, etc.).

If the computing device 102 determines 1014 that the encounter needsfurther clinical review, the computing device 102 may assign 1032 theencounter to a preliminary health care cohort. For example, thecomputing device 102 may store the encounter in a preliminary healthcare cohort table. In some instances, data for classifying encountersinto health care cohorts may be incomplete. Take spine surgery, forexample. The ICD procedure codes may classify all surgeries with 2 to 8levels of fusion in the same code. The cost for a 2 level spinal fusionis quite different than an 8 level fusion. In this situation, spineencounters may be loaded into a health care cohort refiner (e.g., cohortextractor). Additional codes may be added to these encounters in orderto differentiate all the levels of fusions into their own health carecohort. So, instead of having one health care cohort with spinal fusionsfor 2 to 8 levels, there may be 8 health care cohorts specified by theirspinal fusion level (2 level, 3 level, 4 level, 5 level, etc.). Someapproaches to refining health care cohorts are given in connection withFIG. 14.

The computing device 102 may present 1030 the encounter for clinicalreview. For example, the computing device 102 may display the encounterdata and/or may send the encounter data to a user (e.g., health careprovider, etc.). In some configurations, the computing device 102 maysearch all clinical documentation for consistent data elements. Takespinal fusions, for example. One hundred encounters with varying levelsof spinal fusions may be assigned to a preliminary health care cohort.As the 100 encounters are reviewed, the same primary intervention code(from SNOMED, for example) may be utilized to identify the level offusion. So if 25 of the 100 encounters had a two level fusion from L3 toL5, all the 25 encounters may be coded with the same primaryintervention codes to indicate the two level fusion. Consistent dataelements may mean that when additional intervention codes are added toan encounter, a standard coding system is utilized (e.g., ensured) suchthat two different intervention codes are not utilized to represent thesame definition.

The computing device 102 may aggregate data. Some of the encounters maybe assigned to a health care cohort and/or some of the encounters mayneed further review to understand the association of the encounter andthe primary intervention. In some configurations, the computing device102 may return to determine 1004 whether the encounter fits a healthcare cohort definition. For example, the computing device 102 may makethe determination 1004 with updated health care cohort definition(s).

If the computing device 102 determines 1014 that the encounter does notneed further clinical review, the computing device 102 may determine1016 whether the encounter is associated with a predefined health carecohort. For example, the computing device 102 may determine whether theencounter has a primary intervention associated with a health carecohort.

If the computing device 102 determines 1016 that the encounterassociated with the primary intervention is included in a predefinedhealth care cohort, the computing device 102 may assign 1018 theencounter to a health care exhort. For example, the computing device 102may store the encounter in a health care exhort table. A health careexhort may include one or more encounters that should have qualified forthe definition of a health care cohort, but because of one or morecriteria, the encounter is moved to the health care exhort. For example,the appendectomy cohort may only include encounters that had a singlesurgery (appendectomy) during a patient's stay. If a patient also hadtheir gallbladder removed during their appendectomy surgery, thisencounter may be included in an appendectomy health care exhort.

The computing device 102 may determine 1020 whether the encounter hasdata quality issues. This may be accomplished as described in connectionwith one or more of FIGS. 1-2, 4 and 7-9, for instance. For example, thecomputing device 102 may determine whether the encounter has dataquality issues by applying one or more of the approaches for detectingdata quality issues described herein to the encounter.

If the computing device 102 determines 1020 that the encounter does nothave data quality issue(s), the computing device 102 may generate 1012one or more reports. This may be accomplished as described in connectionwith one or more of FIGS. 1-2, 4, 8-9, 12 and 14, for instance. In someconfigurations, the one or more reports may be generated 1012 based onthe health care exhort.

If the computing device 102 determines 1020 that the encounter has dataquality issues, the computing device 102 may reject 1022 the encounter(because the data needs to be cleaned, for example) and/or may reject1024 the health care exhort. For example, the computing device 102 maynot utilize the encounter as valid data. The computing device 102 mayproduce 1026 a data issues report. For example, the computing device 102may generate a report that indicates one or more encounters, health carecohorts, health care exhorts, and/or general billing groups with dataquality issues.

If the computing device 102 determines 1016 that the encounter is notassociated with the primary intervention in a predefined health carecohort, the computing device 102 may assign 1034 the encounter to ageneral billing group. For example, the computing device 102 may storethe encounter in a general billing group table.

The computing device 102 may determine 1036 whether the encounter hasdata quality issues. This may be accomplished as described in connectionwith one or more of FIGS. 1-2, 4 and 7-9, for instance. For example, thecomputing device 102 may determine whether the encounter has dataquality issues by applying one or more of the approaches for detectingdata quality issues described herein to the encounter.

If the computing device 102 determines 1036 that the encounter does nothave data quality issue(s), the computing device 102 may generate 1012one or more reports. This may be accomplished as described in connectionwith one or more of FIGS. 1-2, 4, 8-9, 12 and 14, for instance. In someconfigurations, the one or more reports may be generated 1012 based onthe based on the general billing group.

If the computing device 102 determines 1036 that the encounter has dataquality issues, the computing device 102 may reject 1038 the encounter(because the data needs to be cleaned, for example) and/or may reject1040 the general billing group. For example, the computing device 102may not utilize the encounter as valid data. The computing device 102may produce 1026 a data issues report. For example, the computing device102 may generate a report that indicates one or more encounters, healthcare cohorts, health care exhorts, and/or general billing groups withdata quality issues.

FIG. 11 is a flow diagram illustrating a method 1100 for populating oneor more database (e.g., EDW) tables. For example, the method 1100 mayinclude running a diagnostic process for health care cohorts referred toas Extract, Transform, Load (ETL). The method 1100 may be performed bythe computing device 102 described in connection with FIG. 1. The method1100 may be a more specific example of populating 414 the health carecohort table(s) as described in connection with FIG. 4.

The computing device 102 may begin 1102 the process for updating healthcare cohorts. For example, the computing device 102 may initiate theupdate process and/or may receive an input indicating a command toinitiate the update process.

The computing device 102 may update 1104 the health care cohort mastertable to include new or recently activated defined health care cohorts.For example, the computing device 102 may add one or more new and/orrecently activated defined health care cohorts to a health care cohortmaster table. For example, one or more defined health care cohorts mayhave been defined and/or approved for inclusion in the health carecohorts report.

The computing device 102 may update 1106 the health care cohort codemaster table. For example, the computing device 102 may update 1106 thehealth care cohort code master table with codes of the most recenthealth care cohort definition. Definitions of health care interventiongroups may be recreated by using codes found in the health care cohortcode master. Only codes that are not in the health care cohort codemaster may be used for health care intervention groups in someconfigurations. A health care intervention group may be based onencounters that use codes that are not included in any defined healthcare cohort definition.

The computing device 102 may determine 1110 health care cohorts tocreate. For example, the computing device 102 may determine which of thehealth care cohorts in the health care cohort master table are notalready created (e.g., may determine a list of health care cohorts tocreate).

The computing device 102 may begin 1112 looping in which health carecohorts are created. For example, the computing device 102 may startlooping through the list of health care cohorts to create. The loop mayinclude creating 1114 a defined health care cohort, determining 1116 ifall defined health care cohorts are created and continuing 1118 to loopif all of the defined health care cohorts are not created. For example,the computing device 102 may create 1114 a defined health care cohortbased on the definition in the health care cohort code master table.Each health care cohort may include additional data (e.g., costs,clinical notes, other data, etc.) for each health care cohort encounter.The loop continues 1118 to be repeated until a defined health carecohort is created.

If the computing device 102 determines 1116 that there are no morehealth care cohorts to be created, the computing device 102 may create1120 one or more health care intervention groups. For example, healthcare intervention groups are created 1120 based on the definitiondetermined. All health care cohorts, including defined and undefined maybe complete at this point in some configurations.

The computing device 102 may get 1122 one or more health careprovider(s) for each encounter. For example, the computing device 102may determine the attending provider(s) for each encounter.

The computing device 102 may create 1124 one or more quality assurance(QA) data set bins. For example, the computing device 102 may create QAdata for specific health care cohorts, which isolates encounters withdata quality issues. If the quality issues are found, the computingdevice 102 (e.g., ETL process) may reject the encounter and assign a binnumber. The rejected encounters may be added to a health care cohort QAbin table.

The computing device 102 may get 1126 one or more health care cohortcost(s). For example, the computing device 102 (e.g., ETL process) maydetermine the total cost for each encounter. Data (e.g., provider, QAdata set bin(s) and/or cost) may be added 1128 to the health care cohortbase table.

The computing device 102 may update 1130 health care cohort comments.For example, the computing device 102 may receive, associate and/or addone or more comments (e.g., user comments) to the health care cohort.

The computing device 102 may update 1132 health care cohort outcomes.For example, the computing device 102 may associate (e.g., add, store,etc.) outcomes for each encounter. In some configurations, the ETLprocess may be complete at this point.

Using data update in accordance with above described steps, thecomputing device 102 may populate 1134 the database (e.g., EDW) tables.In some configurations, results may be output to the EDW tables for datareview. In some situations, populating 1134 the database (e.g., EDW)tables may be initiated based on a received input and/or throughcomputational queries and/or ETL procedures.

FIG. 12 is a flow diagram illustrating one configuration of a method1200 for generating one or more reports based on data from database(e.g., EDW) tables. The method 1200 may include generating analyticreports using health care cohort data extracted from EDW tables, forexample. The method 1200 may be performed by the computing device 102and/or electronic device(s) 110 described in connection with FIG. 1. Thecomputing device 102 may extract 1202 data from database (e.g., EDW)tables. For example, the computing device 102 may request (e.g., query)and/or receive health care cohort data from one or more database (e.g.,EDW) tables. The database(s) may be stored on the computing device 102and/or on one or more electronic devices 110.

The computing device 102 may generate 1204 reports. For example, thecomputing device may generate one or more report metrics based on theextracted data. Additionally or alternatively, the computing device 102may load one or more report metrics into a report format (e.g., reporttables, graphs, etc.). Additionally or alternatively, the computingdevice 102 may render the report(s) for display based on the extracteddata (e.g., extracted health care cohort data, metrics based on theextracted data, etc.). In some configurations, generating 1204 report(s)may be accomplished in accordance with the description in connectionwith one or more of FIGS. 1-2, 4, 8-10 and 14.

FIG. 13 is a flow diagram illustrating a method 1300 for assigning ahealth care cohort status. The method 1300 may be performed by thecomputing device 102 in connection with FIG. 1.

The computing device 102 may determine 1310 whether a definition existsfor a health care cohort. For example, the computing device 102 maydetermine whether a health care cohort table in a database (e.g., EDW)includes a definition. If the computing device 102 determines 1310 thatno definition exists for a health care cohort, the computing device 102may add 1304 the health care cohort (e.g., a health care cohortidentifier) to a list for development. For example, the computing device102 may assign a development status for the health care cohort.

If the computing device 102 determines 1310 that a definition exists fora health care cohort, the computing device 102 may determine 1312whether the health care cohort definition has been reviewed and approved(by a health care provider, for example). For example, the computingdevice 102 may determine whether an approval (and/or an indication ofreview) has been received for the health care cohort. If the computingdevice 102 determines 1312 that a health care cohort has not beenapproved (e.g., reviewed and approved) by a provider, the computingdevice 102 may assign 1306 an “under development” status for the healthcare cohort. For example, the computing device 102 may add the healthcare cohort (e.g., a health care cohort identifier) to a list indicatingthat the health care cohort is under development. A health care cohortmay be under development during the data review to identify whatencounter data will and will not be included in the health care cohortdefinition. Development may include reviewing the intervention codetypes (e.g., mandatory code types, allowable code types and/orexclusionary code types). Examples of intervention code types aredescribed in connection with FIG. 6.

If the computing device 102 determines 1312 that a health care cohortdefinition has been reviewed and approved by a provider, the computingdevice 102 may determine 1314 whether the health care cohort definitionis currently being used in reports (e.g., analytic reports). Forexample, the computing device may determine whether the health carecohort is set to be utilized in reports (as indicated by a parameterassociated with the health care cohort, for example).

If the computing device 102 determines 1314 that the health care cohortdefinition is not currently being used in reports, the computing device102 may assign 1308 an “inactive” status for the health care cohort. Forexample, the computing device 102 may add the health care cohort (e.g.,a health care cohort identifier) to a list indicating that the healthcare cohort is inactive. If a health care cohort is inactive, it may notbe used to populate the health care cohort tables in the EDW.

If the computing device 102 determines 1314 that the health care cohortdefinition is currently being used in reports, the computing device 102may assign 1316 an “active” status for the health care cohort. Forexample, the computing device 102 may add the health care cohort (e.g.,a health care cohort identifier) to a list indicating that the healthcare cohort is active.

The computing device 102 may determine 1318 whether the health carecohort definition needs to be revised. For example, reasons for a healthcare cohort revision may include intervention evolution, emergence ofnew health care supplies and equipment or techniques and/or if anindication to refine the existing health care cohort definition isreceived. If the health care cohort needs to be revised, the health carecohort status may be assigned 1306 as “under development.” If the healthcare cohort does not need to be revised, the health care cohort mayremain active.

FIG. 14 is a flow diagram illustrating a method 1400 for refining apreliminary health care cohort (health care prehort). The method 1400may be performed by the computing device 102 described in connectionwith FIG. 1. For example, the cohort creator 118 described in connectionwith FIG. 1 may include a health care prehort refiner (e.g., cohortextractor) in some configurations. The health care prehort refiner mayperform one or more of the operations of the method 1400.

The computing device 102 may perform 1402 a preliminary extraction fromone or more databases based at least on a primary intervention code.This may be accomplished as described in connection with one or more ofFIGS. 1 and 4-5. For example, the computing device 102 may extract(e.g., request, query, retrieve, etc.) one or more encounters with aprimary intervention code (e.g., ICD code, CPT code, etc.) from one ormore databases (e.g., an EDW).

The computing device 102 may store 1404 a set of encounters in a healthcare prehort table. For example, the computing device 102 may store 1404the encounters extracted from the database(s) with a primaryintervention code in a health care prehort table. In someconfigurations, the health care prehort table may be stored in one ormore databases (e.g., an EDW). For example, the health care prehorttable may be stored on the computing device 102 and/or on one or moreelectronic devices 110.

The computing device 102 may generate 1406 a user interface control thatprovides a selection between at least two anatomical options or at leasttwo health care intervention options. For example, the computing device102 may generate one or more controls (e.g., check box(es), drop-downlist(s), radio button(s), button(s), selectable pane(s), selectabletext(s), selectable list(s), etc.) on a user interface (e.g., userinterface 114) that provide a selection between two or more anatomicaloptions (e.g., spine levels such as L1, L2 or L3, etc.) or two or morehealth care intervention options (e.g., abdominal hysterectomy, alaparoscopic hysterectomy, a robotic laparoscopic hysterectomy, etc.).The selection may indicate a classification between different types(e.g., cohorts) of health care interventions. In some configurations,the computing device 102 may generate 1406 one or more user interfacecontrols for anatomical options and health care intervention options.

The computing device 102 may present 1408 an encounter from the healthcare prehort table on a user interface, where the user interfaceincludes the user interface control for the encounter. For example, thecomputing device 102 may present data corresponding to an encounter onthe user interface. In some configurations, the user interface mayinclude the user interface control for the selection (on one area orpane of the user interface, for example) and data (e.g., clinical notes)from the encounter (on another area or pane of the user interface, forexample). For instance, the computing device 102 may present clinicalnotes from an encounter on the user interface.

The computing device 102 may optionally highlight 1410 one or morekeywords in the clinical notes for the encounter. For example, thecomputing device 102 may search the clinical notes for one or morekeywords (e.g., “L1,” “L2,” “L3,” “laparoscopic,” etc.). The computingdevice 102 may highlight (e.g., emphasize, underline, bold, color,circle, present an icon next to, etc.) the keywords. In someconfigurations, the computing device 102 may present (e.g., scroll theclinical notes to) a portion of the clinical notes with a highconcentration (e.g., density) of keywords and/or relevant information.Additionally or alternatively, the computing device 102 may zoom in on aportion of the clinical notes with a high concentration (e.g., density)of keywords. In some configurations, the computing device 102 mayprovide a skip feature that enables jumping from one keyword in theclinical notes to a next keyword based on an input (e.g., mouse click,keyboard key input (e.g., down arrow, “n” for next, etc.)). Thehighlighting (and/or one or more other features such as scrolling,zooming, skipping, etc.) may enable a user to more quickly findinformation that is relevant to the type (e.g., classification, healthcare cohort, etc.) of an encounter.

The computing device 102 may receive 1412 a selected anatomical optionor a health care intervention option. For example, the computing device102 may receive an input (e.g., mouse click, keyboard input, touchinput, pointer input, a network message, etc.) that indicates a selectedanatomical option or a health care intervention option. For instance,the input may indicate a number of levels for a spinal fusion, a type ofhysterectomy, etc. In some configurations, the computing device 102 mayreceive multiple inputs for multiple options (e.g., multiple anatomicaloptions, multiple health care intervention options, both an anatomicaloption and a health care intervention option, one or more of both,etc.).

The computing device 102 may determine 1414 whether the selected optionqualifies the encounter for inclusion in the health care cohort. Forexample, the computing device 102 may determine 1414 whether theselected anatomical option(s) and/or selected health care interventionoption(s) fit the definition of the health care cohort. For instance, ifan encounter is a laparoscopic hysterectomy that is consistent with alaparoscopic hysterectomy cohort, the computing device 102 may determine1414 that the encounter is qualified for inclusion in the health carecohort. However, for instance, if an encounter is an abdominalhysterectomy that is inconsistent with the laparoscopic hysterectomycohort, the computing device 102 may determine 1414 that the encounteris not qualified for inclusion in the health care cohort.

If the computing device 102 determines 1414 that the selected optionqualifies the encounter for inclusion in the health care cohort, thecomputing device 102 may add 1420 the encounter to the health carecohort. For example, the electronic device may store the encounter inthe health care cohort table in a database (e.g., EDW).

If the computing device 102 determines 1414 that the selected optiondoes not qualify the encounter for inclusion in the health care cohort,the computing device 102 may optionally add 1416 the encounter to ahealth care exhort. For example, the computing device 102 may store theencounter in a health care exhort table in a database (e.g., EDW). Ahealth care exhort may be a health care cohort with one or moreencounters that are excluded from another health care cohort. Forexample, a health care exhort may be a health care cohort which includesone or more encounters that have a primary intervention code that is thesame that of another health care cohort, but that have one or moreexcluding factors (e.g., exclusionary secondary interventions, one ormore other parameters that are inconsistent with another health carecohort, etc.).

The computing device 102 may determine 1422 whether there are encountersremaining to refine. For example, the computing device 102 may determinewhether there are any encounters remaining in the health care prehorttable. If one or more encounters remain, the computing device 102 mayproceed 1418 to a next encounter in the health care prehort table. Thecomputing device 102 may repeat one or more steps of the method 1400 foreach remaining encounter. For example, the computing device 102 maypresent 1408 the next encounter from the health care prehort table onthe user interface, as described above.

If the computing device 102 determines 1422 that there are no encountersremaining, the computing device 102 may generate 1424 one or morereports based on the health care cohort. This may be accomplished asdescribed in connection with one or more of FIGS. 1-2, 4, 8-10 and 12.

In some configurations, the method 1400 may be implemented to reduceand/or minimize an amount of time and/or input for classifyingencounters. For example, the computing device 102 may present encountersfrom the health care prehort table in a serial fashion (e.g., one afteranother, one at a time, etc.) in order to allow a user (e.g., healthcare provider, nurse, etc.) to quickly review a number of encounters andprovide input for classifying an encounter. In some configurations, themethod 1400 may classify an encounter based on a single input (e.g.,click). Additionally or alternatively, the method 1400 may automaticallyproceed 1418 to a next encounter after the single input. Accordingly,the method 1400 may ameliorate the burden of requiring a user tomanually look up a set of encounters and/or manually search forinformation relevant to the classification (e.g., health care cohort)for an encounter. It should be noted that the method 1400 may beperformed in conjunction with one or more other methods 200, 400, 500,600, 700, 800, 900, 1000, 1100, 1200, 1300 described herein and/or inconjunction with one or more steps of one or more other methods 200,400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300 described herein.

FIG. 15 illustrates various components of a computing device 1502 thatmay be implemented in accordance with one or more of the systems andmethods disclosed herein. The illustrated components may be locatedwithin the same physical structure or in separate housings orstructures.

The computing device 1502 may include a processor 1540 and memory 1534.The processor 1540 controls the operation of the computing device 1502and may be implemented as a microprocessor, a microcontroller, a digitalsignal processor (DSP) or other device known in the art. The memory 1534may include instructions 1536 a and data 1538 a. The processor 1540typically performs logical and arithmetic operations based on programinstructions 1536 a and data 1538 a stored within the memory 1534. Forexample, instructions 1536 b and/or data 1538 b may be stored and/or runon the processor 1540.

The computing device 1502 typically may include one or morecommunication interfaces 1542 for communicating with other electronicdevices. The communication interfaces 1542 may be based on wiredcommunication technology, wireless communication technology, or both.Examples of different types of communication interfaces 1542 include aserial port, a parallel port, a Universal Serial Bus (USB), an Ethernetadapter, an IEEE 1394 bus interface, a small computer system interface(SCSI) bus interface, an infrared (IR) communication port, a Bluetoothwireless communication adapter, and so forth.

The computing device 1502 typically may include one or more inputdevices 1544 and/or one or more output devices 1546. As stated above,examples of different kinds of input devices 1544 include a keyboard,mouse, microphone, remote control device, button, joystick, trackball,touchpad, lightpen, etc. Examples of different kinds of output devices1546 include a speaker, printer, etc.

One specific type of output device that may be typically included in acomputer system is a display device 1548. Display devices 1548 used withconfigurations disclosed herein may utilize any suitable imageprojection technology, such as liquid crystal display (LCD),light-emitting diode (LED), gas plasma, electroluminescence, a cathoderay tube (CRT), or the like. A display controller 1550 may also beprovided for converting data 1538 a stored in the memory 1534 into text,graphics, and/or moving images (as appropriate) shown on the displaydevice 1548.

Many features of the configurations disclosed herein may be implementedas computer software, electronic hardware, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various components will be described generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application,but such implementation decisions should not be interpreted as causing adeparture from the scope of the present systems and methods.

Where the described functionality is implemented as computer software,such software may include any type of computer instruction or computerexecutable code located within a memory device and/or transmitted aselectronic signals over a system bus or network. Software thatimplements the functionality associated with components described hereinmay include a single instruction, or many instructions, and may bedistributed over several different code segments, among differentprograms, and across several memory devices.

The term “determining” (and grammatical variants thereof) is used in anextremely broad sense. The term “determining” encompasses a wide varietyof actions and therefore “determining” can include calculating,computing, processing, deriving, investigating, looking up (e.g.,looking up in a table, a database or another data structure),ascertaining and the like. Also, “determining” can include receiving(e.g., receiving information), accessing (e.g., accessing data in amemory) and the like. Also, “determining” can include resolving,selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

Information and signals may be represented using any of a variety ofdifferent technologies and techniques. For example, data, instructions,commands, information, signals, bits, symbols and chips that may bereferenced throughout the above description may be represented byvoltages, currents, electromagnetic waves, magnetic fields or particles,optical fields or particles, or any combination thereof.

The various illustrative logical blocks and modules described inconnection with the configurations disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array signal (FPGA) or other programmable logicdevice, discrete gate or transistor logic, discrete hardware components,or any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with theconfigurations disclosed herein may be configured directly in hardware,in a software module executed by a processor, or in a combination of thetwo. A software module may reside in random-access memory (RAM) memory,flash memory, read-only memory (ROM) memory, erasable programmable ROM(EPROM) memory, electrically EPROM (EEPROM) memory, registers, harddisk, a removable disk, a compact disc ROM (CD-ROM) or any other form ofstorage medium known in the art (e.g., such as a non-transitorycomputer-readable). An exemplary storage medium is coupled to theprocessor such that the processor can read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium may be integral to the processor. The processor and the storagemedium may reside in an ASIC. The ASIC may reside in a user terminal. Inthe alternative, the processor and the storage medium may reside asdiscrete components in a user terminal.

The methods disclosed herein include one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of thepresent systems and methods. In other words, unless a specific order ofsteps or actions is required for proper operation of the configuration,the order and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the present systems and methods.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the systems, methods, and apparatus described herein withoutdeparting from the scope of the claims.

What is claimed is:
 1. A method for generating a report by a computingdevice, comprising: receiving an input that indicates a primaryintervention code; identifying a specific health care intervention byperforming a preliminary extraction from one or more databases based onat least the primary intervention code; storing a set of encounters forthe specific health care intervention in a health care prehort table;generating a user interface control that provides a selection between atleast two anatomical options or at least two health care interventionoptions; receiving a selected anatomical option or a health careintervention option; creating a health care cohort for the specifichealth care intervention, wherein the health care cohort comprises adefinition of a primary intervention based on the set of encounters fromthe health care prehort table and the selected anatomical option orhealth care intervention option; performing an encounter analysis toidentify one or more comparative metrics associated with the health carecohort and one or more related health care cohorts, and to determinevariations of the one or more comparative metrics associated with thehealth care cohort and the one or more related health care cohorts,wherein the one or more comparative metrics include costs, and whereinthe variations identify encounters that have exceeded a thresholdproportion of costs more than one standard deviation from an averagecost of the health care cohort; and generating a report based on thehealth care cohort, wherein the report comprises a list of cohorts andthe variations between the health care cohort and the one or morerelated health care cohorts from the encounter analysis, wherein thereport identifies the encounters that have exceeded the thresholdproportion of costs more than one standard deviation from the averagecost of the health care cohort, and wherein the report compares theaverage cost of the health care cohort by region, facility, orphysician.
 2. The method of claim 1, wherein the health care cohortcomprises a list of associated acceptable secondary interventions. 3.The method of claim 2, wherein the health care cohort further comprisesa list of exclusionary secondary interventions that if performed wouldexclude a corresponding encounter from the health care cohort.
 4. Themethod of claim 1, wherein the report comprises a system-specificreport.
 5. The method of claim 4, wherein the system-specific reportcomprises a hospital-specific or site-specific report.
 6. The method ofclaim 1, wherein the report comprises a health care provider-specificreport.
 7. The method of claim 1, wherein the report comprises a list ofcohorts organized relative to a cost variation per encounter compared toan average.
 8. The method of claim 1, further comprising cleaning healthcare cohort data.
 9. An apparatus for generating a report, the apparatuscomprising: a processor; memory in electronic communication with theprocessor; and instructions stored in the memory, the instructions beingexecutable to: receive an input that indicates a primary interventioncode; identify a specific health care intervention by performing apreliminary extraction from one or more databases based on at least theprimary intervention code; store a set of encounters for the specifichealth care intervention in a health care prehort table; generate a userinterface control that provides a selection between at least twoanatomical options or at least two health care intervention options;receive a selected anatomical option or a health care interventionoption; create a health care cohort for the specific health careintervention, wherein the health care cohort comprises a definition of aprimary intervention based on the set of encounters from the health careprehort table and the selected anatomical option or health careintervention option; perform an encounter analysis to identify one ormore comparative metrics associated with the health care cohort and oneor more related health care cohorts, and determine variations of the oneor more comparative metrics associated with the health care cohort andthe one or more related health care cohorts, wherein the one or morecomparative metrics include costs, and wherein the variations identifyencounters that have exceeded a threshold proportion of costs more thanone standard deviation from an average cost of the health care cohort;and generate a report based on the health care cohort, wherein thereport comprises a list of cohorts and the variations between the healthcare cohort and the one or more related health care cohorts from theencounter analysis, wherein the report identifies the encounters thathave exceeded the threshold proportion of costs more than one standarddeviation from the average cost of the health care cohort, and whereinthe report compares the average cost of the health care cohort byregion, facility, or physician.
 10. The apparatus of claim 9, whereinthe health care cohort comprises a list of associated acceptablesecondary interventions.
 11. The apparatus of claim 10, wherein thehealth care cohort further comprises a list of exclusionary secondaryinterventions that if performed would exclude a corresponding encounterfrom the health care cohort.
 12. The apparatus of claim 9, wherein thereport comprises a system-specific report.
 13. The apparatus of claim12, wherein the system-specific report comprises a hospital-specific orsite-specific report.
 14. The apparatus of claim 9, wherein the reportcomprises a health care provider-specific report.
 15. The apparatus ofclaim 9, wherein the report comprises a list of cohorts organizedrelative to a cost variation per encounter compared to an average. 16.The apparatus of claim 9, wherein the instructions are furtherexecutable to clean health care cohort data.
 17. A method for refining ahealth care prehort by a computing device, comprising: performing apreliminary extraction from one or more databases based at least on aprimary intervention code, wherein the preliminary extraction extractsclinical notes; storing a set of encounters in a health care prehorttable; generating a user interface control that provides a selectionbetween at least two anatomical options or at least two health careintervention options; for each encounter in the health care prehorttable: presenting an encounter from the health care prehort table on auser interface, wherein the user interface includes the user interfacecontrol for the encounter; receiving a selected anatomical option or ahealth care intervention option; and adding the encounter to a healthcare cohort if the selected anatomical option or health careintervention option qualifies the encounter for inclusion in the healthcare cohort; performing an encounter analysis to identify one or morecomparative metrics associated with the health care cohort and one ormore related health care cohorts, and to determine variations of the oneor more comparative metrics associated with the health care cohort andthe one or more related health care cohorts; enriching the set ofencounters with the clinical notes; identifying one or more keywords inthe clinical notes that describe differences for each encounter; andgenerating one or more reports based on the health care cohort, whereinthe report comprises a list of cohorts and the variations between thehealth care cohort and the one or more related health care cohorts fromthe encounter analysis, and wherein the report further comprises atleast a portion of the clinical notes with the one or more keywordsemphasized.
 18. The method of claim 17, further comprising highlightingthe one or more keywords in clinical notes for the encounter.
 19. Anapparatus for refining a health care prehort, the apparatus comprising:a processor; memory in electronic communication with the processor; andinstructions stored in the memory, the instructions being executable to:perform a preliminary extraction from one or more databases based atleast on a primary intervention code, wherein the preliminary extractionextracts clinical notes; store a set of encounters in a health careprehort table; generate a user interface control that provides aselection between at least two anatomical options or at least two healthcare intervention options; for each encounter in the health care prehorttable: present an encounter from the health care prehort table on a userinterface, wherein the user interface includes the user interfacecontrol for the encounter; receive a selected anatomical option or ahealth care intervention option; and add the encounter to a health carecohort if the selected anatomical option or health care interventionoption qualifies the encounter for inclusion in the health care cohort;perform an encounter analysis to identify one or more comparativemetrics associated with the health care cohort and one or more relatedhealth care cohorts, and determine variations of the one or morecomparative metrics associated with the health care cohort and the oneor more related health care cohorts; enrich the set of encounters withthe clinical notes; identify one or more keywords in the clinical notesthat describe differences for each encounter; and generate one or morereports based on the health care cohort, wherein the report comprises alist of cohorts and the variations between the health care cohort andthe one or more related health care cohorts from the encounter analysis,and wherein the report further comprises at least a portion of theclinical notes with the keywords emphasized.
 20. The apparatus of claim19, wherein the instructions are further executable to highlight one ormore keywords in clinical notes for the encounter.